{"as_of":"2026-08-10T15:34:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b9e0a8f7fa6576f4d67024331db8868baa3f86d83f86b4c185e2ddd9349fd722","coverage":[{"denominator":75,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":75,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T22:54:34.114513Z","state":"measured"},{"denominator":77,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":77,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T18:40:08.955364Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-11T08:06:00.027311Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.20554","snapshot_observed_at":"2026-08-05T18:40:08.955364Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.14310","last_updated":"2025-08-19T23:22:35Z","snapshot_observed_at":"2026-08-08T14:31:48.056066Z","submitted_at":"2025-08-19T23:22:35Z","title":"Three-dimensional Navier-Stokes-Biot coupling via a moving reticular plate interface: existence of weak solutions","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T18:40:08.955364Z"},"links":{"cited_paper":"/paper/2506.20554","citing_paper":"/paper/2508.14310"},"observation_digest":"sha256:8ec0667c5e3d758483c8864b4e40ac73e67391171f81630158f27f8fa85209f8","observation_id":"2de456d0-a76c-44b5-8632-766cdfb9de99","resolution":{"observed_at":"2026-08-05T18:40:08.955364Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"cited_work":{"arxiv_id":"2506.20554","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.20554","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"91ffcb89-176d-43d3-86d2-fc0a4e644919","year":2025},"citing_paper":{"arxiv_id":"2604.08750","last_updated":"2026-04-09T20:26:31Z","snapshot_observed_at":"2026-07-06T22:57:45.084987Z","submitted_at":"2026-04-09T20:26:31Z","title":"Adversarial Sensor Errors for Safe and Robust Wind Turbine Fleet Control","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-10T16:49:21.194085Z"},"links":{"cited_paper":"/paper/2506.20554","citing_paper":"/paper/2604.08750"},"observation_digest":"sha256:1a43c44e00973be847aa6dca810b9b567f0577d70c160fd0ff5d3264e2027f28","observation_id":"a749bbce-7144-4030-ac69-81e57bd8bf83","resolution":{"observed_at":"2026-05-11T08:06:00.032788Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2506.20554/citation-record","integrity":"/paper/2506.20554/integrity","json":"/paper/2506.20554/citation-record.json","paper":"/paper/2506.20554"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"1910.06403","last_updated":"2020-12-08T19:31:38Z","snapshot_observed_at":"2026-08-08T00:58:02.525456Z","submitted_at":"2019-10-14T20:11:30Z","title":"BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.06403","snapshot_observed_at":"2026-08-06T22:54:26.313453Z","title":"URL https://arxiv.org/abs/1910.06403, 1910.06403","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:26.313453Z"},"links":{"cited_paper":"/paper/1910.06403","citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:f018fac111565564b2a7075faceb972f2d983133dfa34140151f3e157388ee5d","observation_id":"88480f3e-4b83-4206-90e7-43cca448eaab","resolution":{"observed_at":"2026-08-06T22:54:26.313453Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:42.346822Z","title":"Wind Energy 13(6):573–586","venue":null,"work_id":"f89be924-e0c4-435b-a3c6-d1d3d2ca99bb","year":2010},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:26.389927Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:624382e60bea0f7cfbe177b7e8c200beeb40c43806bee370254545344e56b872","observation_id":"fd101078-fa59-4f5c-bc3c-5f36d55eec69","resolution":{"observed_at":"2026-08-06T22:54:42.349850Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:42.338087Z","title":"Wind Energy 12(5):431–444 17","venue":null,"work_id":"56987a1e-92db-425b-b35a-8db14abad86c","year":2009},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:26.484347Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:a9db03ad73d752f6feb5e948ccea4aca05d493f35d3bae0e467b761d6dfac8b5","observation_id":"dd850258-3010-497c-8b47-77c9cc922047","resolution":{"observed_at":"2026-08-06T22:54:42.340881Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:42.329703Z","title":"SoftwareX 12:100550","venue":null,"work_id":"475e1f28-ce04-42be-8f9a-b38c822468a4","year":2020},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:26.587915Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:cc4c11e80a36c0a16cee757a440d9badeb4a6527d0c2cae036a63a169025de6f","observation_id":"d0a906db-9177-495f-bbb4-87b354f2a4c3","resolution":{"observed_at":"2026-08-06T22:54:42.332313Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:42.320137Z","title":"Renewable Energy 70:116–123","venue":null,"work_id":"9d344b62-3c65-4b03-bc44-ff6c5a426af1","year":2014},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:26.740147Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:9fc48c53eefb4f06771e1cd5f5726a44ab4524a171aedf514d39bdb9570b072a","observation_id":"8511dacb-9493-4b51-83aa-b16b2f5f5940","resolution":{"observed_at":"2026-08-06T22:54:42.323006Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:26.936648Z","title":"URL https: //doi.org/10.5281/zenodo.14507040","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:26.936648Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:dcdaba58d235d1f6163fdc200e14ffacb11b7f11aff41471445a510522ce4448","observation_id":"0db856a0-749a-4850-87b1-461a5bec221b","resolution":{"observed_at":"2026-08-06T22:54:26.936648Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:42.311257Z","title":"Journal of Fluid Mechanics 955:A12","venue":null,"work_id":"8b18e10f-c389-416c-9bdb-a8616b40777f","year":2023},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:27.083514Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:4296e97b8616771f12df97e3216c45cc7311886a0c48e8c35e1beb376b6cdb14","observation_id":"3ba4b8ab-68f7-418a-a5cc-091e714d4ede","resolution":{"observed_at":"2026-08-06T22:54:42.314177Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:42.302443Z","title":"Wind Energy Science 9(4):869–882","venue":null,"work_id":"85ec2c91-6db6-44de-9fbf-a03b696ad52b","year":2024},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:27.234637Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:149a45de3d48b4a6d41d894811663fdc9298a827e441405a0d02d59d773ffb38","observation_id":"ae866de2-fa6b-48ec-b523-57c46eccaeba","resolution":{"observed_at":"2026-08-06T22:54:42.305154Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.00577","last_updated":"2023-11-27T15:57:06Z","snapshot_observed_at":"2026-08-09T16:10:30.032242Z","submitted_at":"2023-06-01T11:45:45Z","title":"TorchRL: A data-driven decision-making library for PyTorch","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.00577","snapshot_observed_at":"2026-08-06T22:54:27.361844Z","title":"2306.00577","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:27.361844Z"},"links":{"cited_paper":"/paper/2306.00577","citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:23a8ce7d2b9b09d6f67c2aad696b04331afc4b6e06bf1db9d9574797d66a48b0","observation_id":"c97fb153-3de3-4c4d-90b6-c73dafa6239b","resolution":{"observed_at":"2026-08-06T22:54:27.361844Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:42.293362Z","title":null,"venue":null,"work_id":"3a6652af-bdbc-4b5f-be81-a407a727d34e","year":2020},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:27.547243Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:611feb74f42308ad1797e3a9424e45116fdb0f1b89261e90180f5b5aced4cfdc","observation_id":"92ed5997-1673-4181-ae48-af98792f70cb","resolution":{"observed_at":"2026-08-06T22:54:42.296199Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:42.284200Z","title":"Physics of Fluids 22(1):015110","venue":null,"work_id":"bc7ff025-1a22-4f9f-88cd-5755790f3dbc","year":2010},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:27.715224Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:96daad41b770d0dcc381ef117b21cfd7d06b495d721b7941e26a7f0c77bff204","observation_id":"b1f691b0-0e42-4f3f-8d76-d6d840efa9d0","resolution":{"observed_at":"2026-08-06T22:54:42.287486Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:42.274623Z","title":"In: Kuerten H, Geurts B, Armenio V, et al (eds) Direct and Large-Eddy Simulation VIII","venue":null,"work_id":"81078bae-79f6-45b0-bf6d-f0d595927978","year":2011},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:27.885945Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:6f0a6999c9c0eecd29c8896b10918534c5fd78116fc5762568aa090173037b48","observation_id":"cb682a8e-e5f9-4678-a5da-6a9a41201dbf","resolution":{"observed_at":"2026-08-06T22:54:42.278070Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:42.264312Z","title":"Physical Review Fluids 9(4):043902","venue":null,"work_id":"a2568524-de9f-4ed3-a53c-f29ba902b963","year":2024},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:28.047392Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:2ba6076e17571f6bd3e1121230a05e6288de5741accd5ac22c935fc61047876d","observation_id":"0ceaeaff-a73c-456c-bbd9-c3bac8d91c28","resolution":{"observed_at":"2026-08-06T22:54:42.267828Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:42.255057Z","title":"Nature 602(7897):414–419","venue":null,"work_id":"f3ae548b-abef-4b34-b5ff-7f3421321ab2","year":2022},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:28.182971Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:925f449ade7493626e726db721e812aa25fb42a13ce609c5715339ec90ffa919","observation_id":"ea5ae774-4a04-4d68-8574-74689bc7d377","resolution":{"observed_at":"2026-08-06T22:54:42.258108Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:42.245018Z","title":"Wind Energy","venue":null,"work_id":"09cd4e22-9497-449d-a989-982ab7fc6778","year":2020},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:28.330666Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:25e8ec3a04d83b66e55e50a282036277b827854ded50f704c506a321a719a8c1","observation_id":"076495bb-8a9b-4abd-ac79-594398aaf284","resolution":{"observed_at":"2026-08-06T22:54:42.248368Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:42.235005Z","title":"Applied Energy 292:116928","venue":null,"work_id":"32329f29-d81e-4fd0-84a1-59fe7ad3b3c7","year":2021},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:28.474409Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:8952df24ec8c91e7b1c77b9d835d1da99d04b2248dcbd56ac2525321ab5b8bda","observation_id":"71cbc9bb-2e02-43dd-914f-f161030436d9","resolution":{"observed_at":"2026-08-06T22:54:42.238472Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:42.225552Z","title":"Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 476(2238):20200097","venue":null,"work_id":"5672872e-9e75-46e3-96fb-47d3d9de1b95","year":2020},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:28.535816Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:25b3d5e04853eb569c11de06c86351df29bc1cf4c0228edb2be7920459648e10","observation_id":"8e7045e1-795a-4564-b85e-c9fb7576f454","resolution":{"observed_at":"2026-08-06T22:54:42.228744Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:42.215824Z","title":"Nature Communications 16(1):1422","venue":null,"work_id":"c71018b6-d359-4e0a-bbc7-3ea56c1dd1b3","year":2025},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:28.756900Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:5b6c550954c521067c5c619b0653cdfc5c48c4d5bccbb5b37cf89837bc3a7592","observation_id":"93358dc2-a0e6-49f0-95b7-c101cdf95f62","resolution":{"observed_at":"2026-08-06T22:54:42.218771Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:42.206620Z","title":"Wind Energy 23(8):1739–1751","venue":null,"work_id":"c2679be8-99e6-486b-9a3a-446c5dcde9bf","year":2020},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:28.854723Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:62840d9cf3879ad731a015dd03a507a31c335722daf737084a249873216d1010","observation_id":"2b6f9913-4362-482d-9141-55155e3878fd","resolution":{"observed_at":"2026-08-06T22:54:42.209475Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:42.197164Z","title":"In: International conference on machine learning, PMLR, pp 1587–1596 18","venue":null,"work_id":"aae37324-609f-4f08-99ef-16d1fa5bde35","year":2018},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:28.951897Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:e6b115f28096974e77613b6584928a3802a4f340f4fe71ffdf7cc58292f742b6","observation_id":"cb87cd2a-ab88-4055-968f-c41bee3e260d","resolution":{"observed_at":"2026-08-06T22:54:42.200456Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:42.187592Z","title":"Nature Communications 14(1):6480","venue":null,"work_id":"2e5d63a4-7844-4bf9-873c-08d14eb6b389","year":2023},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:29.050890Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:597e3cbb5e07fa3dfb60213a7b0b46795b829fc1b4d0f1afe7658881c1a1799b","observation_id":"f2e45bb0-e90b-4bdc-b7ee-8930cde84774","resolution":{"observed_at":"2026-08-06T22:54:42.190952Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:42.178695Z","title":"Journal of Fluid Mechanics 768:5–50","venue":null,"work_id":"bf110dd0-048f-4e47-b655-47447bbc6561","year":2015},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:29.136179Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:0f4c08ad9bf7a378100063854bd36400592c600b57ca942ae7485589ea38449a","observation_id":"bbc78d07-13b7-47a5-bae0-27dcf661127c","resolution":{"observed_at":"2026-08-06T22:54:42.181568Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:42.169176Z","title":"In: International conference on machine learning, PMLR, pp 1861–1870","venue":null,"work_id":"3b14302c-794a-4284-bc11-6c244c93b193","year":2018},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:29.221592Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:c35dce3cad43a77eacecb74158c82337b32fc2a05b95bdc080e8173a2db6a081","observation_id":"4a9b2901-f5f5-40bf-b96a-d60780c34f5d","resolution":{"observed_at":"2026-08-06T22:54:42.172186Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:42.159877Z","title":"Frontiers in Neurorobotics 17:1127642","venue":null,"work_id":"a532d810-60e2-4c0e-a670-9bfe81faab28","year":2023},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:29.327470Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:43e070d967cc0d89cc241f74b75dd2e2ceb08c7f650f7b80548b902a3c96f7dc","observation_id":"2592c642-b5f0-4b9f-8b2c-38a157baa8bf","resolution":{"observed_at":"2026-08-06T22:54:42.162922Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:42.150387Z","title":"Nature Energy 9(6):735–749","venue":null,"work_id":"587369dc-8f14-45ae-bc03-b3f49316cf84","year":2024},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:29.413711Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:3fa5d012435987889869b9052ed3cda6f70827ac11ce5529424c1bfaad45099d","observation_id":"6f495794-cc4c-4df5-80a9-9fcb8ee6d2df","resolution":{"observed_at":"2026-08-06T22:54:42.153870Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:42.140163Z","title":"International Journal of Electrical Power & Energy Systems 143:108406","venue":null,"work_id":"c2e7cf56-0cf8-4aeb-a7dd-74bf89881613","year":2022},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:29.505467Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:899cfea82e8f0ff12a931eea64388e40b154b37a4dacf40496d6d2c7b5f0eba8","observation_id":"781f7e0b-2d19-42f3-9913-079be44b9906","resolution":{"observed_at":"2026-08-06T22:54:42.143352Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:42.129693Z","title":"Wind Energy 25(2):195–220","venue":null,"work_id":"a2180ba9-7765-45f0-bbf0-f22aaaccf70d","year":2022},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:29.602069Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:85664e0033cc9b9d455c32e0688139f1bc7ae4de5fdfaacf8976a17126386177","observation_id":"51d7a3e4-5479-4b98-ae4c-52cabe38ef60","resolution":{"observed_at":"2026-08-06T22:54:42.133293Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:42.118162Z","title":"MIT Press 2:39–47","venue":null,"work_id":"9169b158-fead-4f0b-bd72-73e88a09bddc","year":1960},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:29.704516Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:d81b222733add9d0ca42a914ceb4e22cc54c482ddcd428c0c246cd1fb0a57087","observation_id":"5a18cdd9-8602-4b4f-8e15-70a44a046ec0","resolution":{"observed_at":"2026-08-06T22:54:42.122349Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:42.108843Z","title":"Wind Energy Science 5(4):1315– 1338","venue":null,"work_id":"1c1f88f8-7bde-4fff-bb8e-5d0aaff53aff","year":2020},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:29.806244Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:181e3ff8fb6e9ab6eeba1c50fbe7419591e11250baead0924fef24de0c6c7d8b","observation_id":"372b7839-10eb-4215-994e-9bd6f4ea9423","resolution":{"observed_at":"2026-08-06T22:54:42.112108Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:42.099181Z","title":"Nature Energy 7(9):818–827","venue":null,"work_id":"94eca8de-49a7-4a7c-8590-ddfafb39b07a","year":2022},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:29.997085Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:3e4488456b3f999dc3f3bca3c92739273f763026027f9c92bf423f9e12e085cf","observation_id":"930e0dcc-3e0d-4cde-bc7d-40e9db7b0c72","resolution":{"observed_at":"2026-08-06T22:54:42.102550Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:42.089918Z","title":"URL https://www.iea.org/reports/net-zero-by-2050","venue":null,"work_id":"813ec8a2-5f3c-4334-89ff-cb189805b330","year":2021},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:30.112482Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:6b814d1943d458ff5ab2ee6ef24726c98090555a8e7e0a44bf6d71669be054a6","observation_id":"a0dde3f7-4e12-4659-b1ce-c284ad6d5c0d","resolution":{"observed_at":"2026-08-06T22:54:42.092771Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:42.080515Z","title":"Wind Energy p e2946","venue":null,"work_id":"deb94ac2-3390-48bd-acbc-138e57c722ef","year":2024},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:30.235760Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:98a1bc2e958e030dbc8eb65e637e816940ba26b2205cf2a71eb38c62035599b6","observation_id":"019d9463-b864-4cfe-b06e-7815643cfb82","resolution":{"observed_at":"2026-08-06T22:54:42.083478Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:41.889900Z","title":null,"venue":null,"work_id":"bb20977a-f807-4489-89fe-56183789d677","year":1983},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:30.324437Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:591690bd5313578761e7b4dbdfcd612d927c40715777c2c3a2fa74a3cc940c02","observation_id":"76467d2b-708d-4ec0-8d40-f52001d59d00","resolution":{"observed_at":"2026-08-06T22:54:42.043950Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:41.514458Z","title":"Wind Energy 13(6):559–572","venue":null,"work_id":"76d90901-05cc-4727-800b-863c7eb4c47a","year":2010},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:30.454943Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:bc745a4783b68da589c87fe3bfbadd0f26b2d80c214a4b2a3d83dbadf851338a","observation_id":"2d200abb-0ca2-4625-8f79-b5c2aeab925e","resolution":{"observed_at":"2026-08-06T22:54:41.667425Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:41.333630Z","title":"Journal of Global optimization 13:455–492","venue":null,"work_id":"c2a7ddfa-7e80-47e2-ab39-52c42c317a88","year":1998},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:30.520120Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:850f11d5196ec81b4360ced4ea2d77bbeb38c4e9c565d3232c38eadc4bc165c5","observation_id":"042adec8-87c3-43ba-94e5-b90c9f207d5a","resolution":{"observed_at":"2026-08-06T22:54:41.407360Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:41.068339Z","title":"Wind Energy Science 2(1):115–131","venue":null,"work_id":"17329e45-ee21-4d3f-bb0d-3f4f40db14b7","year":2017},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:30.598645Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:eb4379ca07a2d4da4a86dc3cfacfaeeb0b0aa5cd3cbae36d1867b59c0f1a6706","observation_id":"4a826988-486e-4142-90ee-1de505051dc9","resolution":{"observed_at":"2026-08-06T22:54:41.216336Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:40.835003Z","title":"The International Journal of Robotics Research 32(11):1238–1274 19","venue":null,"work_id":"301351da-bebc-4841-a797-8e4094b926c0","year":2013},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:30.706064Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:ddae745dfc20e7fd931d1b801f2c425a7959e0660d04c7f909efe3dd1da0ef1b","observation_id":"253c2674-c76f-499c-be34-30a6385f5069","resolution":{"observed_at":"2026-08-06T22:54:40.948127Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:40.813574Z","title":"Journal of Physics: Conference Series 1934(1):012022","venue":null,"work_id":"600cc01d-db6c-441d-be6d-9c6211f54869","year":2021},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:30.794579Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:447f78e6ae270b76f3672eb93df58e041d985374083baa9a45a350e7830a6c87","observation_id":"b1839d3c-6f68-459f-9345-c0ecc26340d0","resolution":{"observed_at":"2026-08-06T22:54:40.816755Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:40.714467Z","title":"Journal of Computational Physics 228:5989–6015","venue":null,"work_id":"629c3a5b-5ff3-4368-b2c2-55ed9214341d","year":2009},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:30.884841Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:db20ead14c2984c9aeee64fbc56f8af4e44fe16ba8d8a3819a6efe16d00ee061","observation_id":"25be18c8-fa34-4f3a-9893-7ed86f3cf74c","resolution":{"observed_at":"2026-08-06T22:54:40.772515Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:40.624333Z","title":"International Journal for Numerical Methods in Fluids 67:1735–57","venue":null,"work_id":"b5723bf6-268f-413c-8856-1c3148fe4ccb","year":2011},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:30.971797Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:5e4a2760747d7d14cb209d918ba3c2b1f4297d84c26ccb5c4d44e3c8fda3f8ae","observation_id":"b3dfceba-ef70-4825-9073-ba59c1911860","resolution":{"observed_at":"2026-08-06T22:54:40.671097Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:40.500590Z","title":"arXiv preprint arXiv:150902971","venue":null,"work_id":"c54dffa3-d250-435a-a535-25514f5aa9d8","year":2015},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:31.080646Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:7c35bac9854a10501dd228b706b41dec5d047c88212667ad3d1d045f129009ee","observation_id":"58655d8f-a607-44e7-ab35-9738055d83d2","resolution":{"observed_at":"2026-08-06T22:54:40.569754Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:40.379377Z","title":"Journal of Fluid Mechanics 242:51–78","venue":null,"work_id":"94891d2f-0c66-4e0b-997b-152bc27d8c6e","year":1992},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:31.159146Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:6dcc26bf1a1e3ef3563351c0e2446d05eb923bd01c2de3ebfeb21b1b17dfc32a","observation_id":"a13560d8-4cd1-4df4-9d50-3e182e35a782","resolution":{"observed_at":"2026-08-06T22:54:40.428571Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:40.287278Z","title":"Wind Energy 15(2):305–317","venue":null,"work_id":"a637d811-66a3-405e-84f5-1978ad61de73","year":2012},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:31.269032Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:116522d7adbb36f300a9b39b2b07191ddf666f8a559dcf4fcea3151e8826bc91","observation_id":"dd1008d6-9c49-4d5d-9b7c-1b51d1357187","resolution":{"observed_at":"2026-08-06T22:54:40.328641Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:40.135078Z","title":"Wind Energy Science 7(6):2271–2306","venue":null,"work_id":"6b518bf7-fce9-4d32-81a8-7bd482608862","year":2022},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:31.382995Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:9913e56a6fd599bd7fa86e60fecc734b3d3a063c529fdeda5355651d0a68d50f","observation_id":"f6542227-f828-4433-8f2f-496a922dd7ad","resolution":{"observed_at":"2026-08-06T22:54:40.213936Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:40.023862Z","title":"PhD thesis, Technical University of Denmark","venue":null,"work_id":"8a3ed577-4a91-40d9-bcb6-76ef7a0ed674","year":2004},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:31.474241Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:02c0013c1162d5af831eb3e302697197e195627fdb41551ec5933f468e0a8a24","observation_id":"6c0f696e-83aa-433d-98ba-95c44346bed4","resolution":{"observed_at":"2026-08-06T22:54:40.075213Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:39.884986Z","title":"arXiv preprint arXiv:13125602","venue":null,"work_id":"d4ecc381-6671-4a2c-a58a-293f6d3c67f6","year":2013},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:31.584513Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:110213566d7847c094bb10206a5dfc1ba8317f1bacf662348e9c0bfd8eb4f6c4","observation_id":"0345547e-4238-4c44-8eb6-77486d6dc071","resolution":{"observed_at":"2026-08-06T22:54:39.945418Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:39.763890Z","title":"nature 518(7540):529–533","venue":null,"work_id":"5ab50e48-16c5-4d3a-8cbf-37b13a029771","year":2015},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:31.672200Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:0193ae2f70a99ce49270d60d49903c2976d87b8a383731e7958e20108ae9a2ef","observation_id":"b1091ec0-3ea1-448d-9725-ed076e0876dc","resolution":{"observed_at":"2026-08-06T22:54:39.813003Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:39.601677Z","title":"In: International conference on machine learning, PmLR, pp 1928–1937","venue":null,"work_id":"3bb076e3-d217-42dd-9feb-ea4599f8255b","year":2016},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:31.759296Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:1739bd8acdfa78c2fd85a64945ef2fb69a5d310544386523830f4041acd4eb1a","observation_id":"b6e935d6-0a38-4aa1-a74c-19e5caeb3e50","resolution":{"observed_at":"2026-08-06T22:54:39.684477Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:39.460312Z","title":"Flow, Turbulence and Combustion","venue":null,"work_id":"81f88f01-2825-46c9-9413-454f2724029f","year":2024},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:31.849998Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:bf950b4645ac71ba9362c1baaadb86abed037f448126ad08dab9385b598948fc","observation_id":"cc781757-24f9-43a3-b2f2-b244a53308a0","resolution":{"observed_at":"2026-08-06T22:54:39.529830Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.13592","last_updated":"2025-01-23T12:01:17Z","snapshot_observed_at":"2026-07-06T20:24:55.277911Z","submitted_at":"2025-01-23T12:01:17Z","title":"WFCRL: A Multi-Agent Reinforcement Learning Benchmark for Wind Farm Control","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.13592","snapshot_observed_at":"2026-08-06T22:54:31.942277Z","title":"2501.13592","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:31.942277Z"},"links":{"cited_paper":"/paper/2501.13592","citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:1b9883338e9de0f39fbf679dd9d4bbdd57e8e3b7149a185e79192508410acd25","observation_id":"f2c289ca-40b9-47e6-a0ca-5aa02ad31548","resolution":{"observed_at":"2026-08-06T22:54:31.942277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:39.230670Z","title":"Energies 11(1):177","venue":null,"work_id":"e7ead3db-8bdc-459f-8cac-4af61f97a8f0","year":2018},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:32.009404Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:ab1ca5d7135009dfca8799cdc1695a47eec73af753481060ed3109ce3225e653","observation_id":"ac896b0c-3fb3-461b-a39d-e9c78f23f0a0","resolution":{"observed_at":"2026-08-06T22:54:39.330985Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:39.062605Z","title":"Physics of Fluids 28(2):025112","venue":null,"work_id":"fe455946-56d2-4d26-b3da-ad742d5170a8","year":2016},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:32.083932Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:6b705c9094286ae6790dd013dd2b8328c708da5edb7cf754828cb783f578da26","observation_id":"b3990415-b365-4e2b-9352-4ad24dafd29f","resolution":{"observed_at":"2026-08-06T22:54:39.119873Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:38.904268Z","title":"Renewable Energy 181:445–456","venue":null,"work_id":"aee45367-e725-48ef-9de7-281231bc6ea4","year":2022},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:32.164330Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:be4e1504feecc34dada0e02f189ddfaf45679c5bcb664a8d5c475b46d6b83688","observation_id":"5e586e23-a2b4-4695-a50b-a4fb576b13e6","resolution":{"observed_at":"2026-08-06T22:54:38.970498Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:38.729164Z","title":"Journal of Computational Science 62:101707","venue":null,"work_id":"cbb6e5bb-4c05-4a93-9ffc-dd21a9fb9bb5","year":2022},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:32.251159Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:5a6e42737b876f3043fd6f35ef6eed67c21b87e020c84a7773099fe436e443e3","observation_id":"46976d5c-4099-4400-ad77-0547a2f75aa6","resolution":{"observed_at":"2026-08-06T22:54:38.820663Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:38.555653Z","title":"Advances in neural information processing systems 32","venue":null,"work_id":"1fd7dc08-54ef-40fa-a3cd-0a0e329f61f8","year":2019},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:32.340622Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:52f758a9168880b504108f45bf05720d9d4746694ec9a0c3e8b468819d9e7861","observation_id":"a6120853-8d83-4030-92ad-9a866fa081e8","resolution":{"observed_at":"2026-08-06T22:54:38.625203Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:38.382803Z","title":"Adaptive Computation and Machine Learning, MIT Press, Cambridge, Mass","venue":null,"work_id":"1372f84a-f748-414b-b60b-de849568be5b","year":2008},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:32.432190Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:66bec4dcb3ef393d60962ce71b069ffe74241e366efcc41bb04c02892f39c116","observation_id":"51ff2d28-b7f6-48c0-9c2f-38b83bfac996","resolution":{"observed_at":"2026-08-06T22:54:38.464196Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:38.222108Z","title":"Energies 14(13):3745","venue":null,"work_id":"b55fa227-e65d-46c2-8c05-dc592afba651","year":2021},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:32.546508Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:f9d9a66540e069f78caa3161be44141e873d02b5b685b9904c417d315624b975","observation_id":"7feccc53-ae93-4484-83ac-ec642169a17b","resolution":{"observed_at":"2026-08-06T22:54:38.322069Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:32.616182Z","title":"arXiv preprint arXiv:170706347","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:32.616182Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:9cc8f78bffeca212dca2f3a41cbbdd2aa139bae1405f2e08005d76d848c2d86d","observation_id":"25b08fa5-9adc-4a96-a7b8-9c1aad3990d5","resolution":{"observed_at":"2026-08-06T22:54:32.616182Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:38.036033Z","title":"Proceedings of the IEEE 104(1):148–175","venue":null,"work_id":"4dfad8be-9209-4fab-a10c-865041428e9c","year":2016},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:32.700003Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:b1ba31efe06e5750355e3d43d3383c2fac611c3edebf01d3f4eff29b0cc28262","observation_id":"56a9de49-4686-48b4-bfbf-a9fcd78af037","resolution":{"observed_at":"2026-08-06T22:54:38.130796Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:37.857546Z","title":"In: Proceedings of European Wind Energy Conference 2007","venue":null,"work_id":"cec284e7-fb74-4b03-b6c7-6a0ff21c815a","year":2007},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:32.803866Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:3157c31a0ac00d0bf96dcc703f4e5cb4750317eb059f7d0af596912915db7d71","observation_id":"95cb1a89-b8b1-47f6-805f-e004a02be1ac","resolution":{"observed_at":"2026-08-06T22:54:37.922221Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:37.556984Z","title":"Monthly Weather Review 91(3):99–164","venue":null,"work_id":"0716f51c-128d-47c1-ad3f-d189be5a2eb2","year":1963},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:32.897939Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:9d219f2543da6646ca409d27b03530ff39d9aa39b01cff29c5906f4d65ca9ca9","observation_id":"6854cc18-53a9-4137-94e9-db08801c0434","resolution":{"observed_at":"2026-08-06T22:54:37.716944Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:37.285945Z","title":"In: Pereira F, Burges C, Bottou L, et al (eds) Advances in Neural Information Processing Systems, vol 25","venue":null,"work_id":"c28618cf-3d60-46ec-baa4-318ba225021a","year":2012},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:32.969421Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:bc6bce4ba5887104d9d7402161d2c68106702c8f36626c8c3508e22868e8639a","observation_id":"2181738f-7f4d-42d0-b2a0-01ea339074fd","resolution":{"observed_at":"2026-08-06T22:54:37.439850Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:37.003771Z","title":"Journal of Fluids Engineering 124(2):393–399","venue":null,"work_id":"3da90586-76d2-41e6-93bd-7bfa66d73a36","year":2002},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:33.076741Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:e030e18e5cc7b04be53cae269b0e6dda67afae00ad28d4979a7bdfabefa1c56f","observation_id":"152d7109-213f-4618-b2f2-6eb10b478d7d","resolution":{"observed_at":"2026-08-06T22:54:37.159620Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:36.696456Z","title":"arXiv preprint arXiv:09123995","venue":null,"work_id":"d54ac82a-6456-4547-83b0-33128cf13311","year":2009},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:33.191974Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:09780e9214f7be85682bccc45305f920dfcb6a7b9e568afe4f71fef2dea04abd","observation_id":"28d72646-ce9a-4d5d-88e4-5915cfd278fe","resolution":{"observed_at":"2026-08-06T22:54:36.832576Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:36.420868Z","title":"Machine learning 3:9–44","venue":null,"work_id":"3cf41daf-0215-4a68-9bf6-9664e41d1bd1","year":1988},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:33.260469Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:ed21c1722f4c162460b889fe7d7a89840c83089d3b5ae7374ebf3106a882d120","observation_id":"30ae87a3-52b4-4a00-8237-a0638e268d92","resolution":{"observed_at":"2026-08-06T22:54:36.555306Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:36.157734Z","title":"MIT press","venue":null,"work_id":"736c0574-0ada-4eb6-9a52-abeb945ded84","year":2018},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:33.330546Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:699475805640a67ac65379e3b17e9f3bb9ca72ff1fc1d5bc66e5b24daecd07ff","observation_id":"fb505935-bd19-4234-bd59-7309dcf4fbc4","resolution":{"observed_at":"2026-08-06T22:54:36.220282Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:35.968836Z","title":"In: Proceedings of the AAAI conference on artificial intelligence","venue":null,"work_id":"e54bf42f-4e59-410d-b2b2-3bfc129c267d","year":2016},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:33.417120Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:23213128e28cffd9686b5590444e976cc2cb1fb8627745141e404c79d68ec31e","observation_id":"394b7dab-d8b6-4592-93e4-ce59dc17efbd","resolution":{"observed_at":"2026-08-06T22:54:36.068774Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:35.833152Z","title":"Wind Energy Science 8(7):1071–1131","venue":null,"work_id":"3d2f7afd-d033-407d-8ad5-7be812664fb3","year":2023},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:33.522723Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:c4746e6fd12ff3e9ec8a54ec6175a4a205579ae78dea53487fed2d19a4822b20","observation_id":"c32f09de-26c2-4e52-b201-e950d7092855","resolution":{"observed_at":"2026-08-06T22:54:35.895612Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:35.691070Z","title":"Physics of Fluids 35(3)","venue":null,"work_id":"85d29b2c-8ed7-4fbd-a37e-06949cea9383","year":2023},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:33.607198Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:78863504636776d77ec2f928046d024878a041f8ee9a9fd24aa01af3e471027b","observation_id":"b7ab819a-ac4d-427d-940b-572d05c99cff","resolution":{"observed_at":"2026-08-06T22:54:35.763697Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:35.492068Z","title":"Energy Conversion and Management 337:119888","venue":null,"work_id":"ccf69430-d9d4-4764-b5cb-8d49d5518a6f","year":2025},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:33.679893Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:25de6c05079888456d480f22a782c5af3970823444cd7d2bacc661c1ef75bb54","observation_id":"9b5dceb9-b1b9-4736-b542-2a88ce5a3c9d","resolution":{"observed_at":"2026-08-06T22:54:35.596582Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:35.296213Z","title":"Machine learning 8:279–292","venue":null,"work_id":"35121894-08bb-49e9-a420-affb78275eb5","year":1992},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:33.760267Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:2a507400bf8fffc8fd7ef6ef147d6893b1d3ccd08ac7f1ff08ab3051f5a5d98b","observation_id":"8a711f2a-ab75-4617-a017-0330d3654563","resolution":{"observed_at":"2026-08-06T22:54:35.403061Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:35.046665Z","title":null,"venue":null,"work_id":"5d68e8be-5e45-4829-a65d-ec0912f4e315","year":1989},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:33.800809Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:89c6198e4ef5b3670077b28d28ae152706c89be88bcbb7bc9125545140f621a6","observation_id":"7e8efbe9-0370-45d3-be9d-a886f0ecdcf0","resolution":{"observed_at":"2026-08-06T22:54:35.161401Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:34.798534Z","title":"Renewable Energy 75:945–955","venue":null,"work_id":"00ad18a6-c633-4da1-8314-80803cfcbebb","year":2015},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:33.909018Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:3b327c21625856fe9aa41350c656ac9d403240ba7fe428681e49d771005e1ace","observation_id":"d4495f91-eabf-4b5e-9b5c-b45e9a215483","resolution":{"observed_at":"2026-08-06T22:54:34.908818Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:34.572981Z","title":"Journal of Fluid Mechanics 981:A17","venue":null,"work_id":"49ba9c66-b70c-4dbc-a5f5-ab1d11d236fe","year":2024},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:34.017928Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:079e1571e19f027401f1af266148f509e68951c1354ef80bedc3133e64fe1cbe","observation_id":"bae639cc-9890-4233-8316-735615d84dd0","resolution":{"observed_at":"2026-08-06T22:54:34.677050Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:34.323053Z","title":"Real-time digital optimisation and decision making for energy and transport systems","venue":null,"work_id":"fcd6b4ea-bd8b-4974-b233-12fbd4476886","year":2022},"citing_paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:34.114513Z"},"links":{"citing_paper":"/paper/2506.20554"},"observation_digest":"sha256:ea3e9267b3dc89391bb24e14de16186346bcf11a88205f0bd88ed8df5442bea3","observation_id":"f127f1fc-7feb-4583-97a7-b7908ee1235e","resolution":{"observed_at":"2026-08-06T22:54:34.429317Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.20554","last_updated":"2025-06-25T15:53:12Z","latest_version":1,"primary_category":"physics.flu-dyn","snapshot_observed_at":"2026-08-09T05:49:27.223102Z","submitted_at":"2025-06-25T15:53:12Z","title":"Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control"},"reference_resolution":{"displayed":75,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":8,"verified_exact":0,"verified_fuzzy":67},"total_outbound_references":75},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 75 of 75 outbound references and 2 inbound Pith citation observations for arXiv:2506.20554."}