{"as_of":"2026-08-18T08:19:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:08cbcba9cf3d0153f940bf1bda1713dbbe12d57646eaa33ec79d21f8caa9576f","coverage":[{"denominator":22,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":22,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T15:28:35.426260Z","state":"measured"},{"denominator":22,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":22,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2411.14192/citation-record","integrity":"/paper/2411.14192/integrity","json":"/paper/2411.14192/citation-record.json","paper":"/paper/2411.14192"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T15:28:34.994750Z","title":"Masson-Delmotte, P","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.14192","last_updated":"2024-11-21T15:01:17Z","snapshot_observed_at":"2026-08-16T18:06:06.874752Z","submitted_at":"2024-11-21T15:01:17Z","title":"Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T15:28:34.994750Z"},"links":{"citing_paper":"/paper/2411.14192"},"observation_digest":"sha256:fbb7b6416d0ba3a779b51896f157f1ef4179d9adaf11d480ed60ae13d023be05","observation_id":"b6857160-d518-4b9a-b0c2-e46ee503d43e","resolution":{"observed_at":"2026-08-12T15:28:34.994750Z","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":"2026-08-12T15:28:36.202155Z","title":"Progress in carbon capture technologies","venue":null,"work_id":"78550cf4-084b-44e7-a80d-4df122afd7a8","year":2021},"citing_paper":{"arxiv_id":"2411.14192","last_updated":"2024-11-21T15:01:17Z","snapshot_observed_at":"2026-08-16T18:06:06.874752Z","submitted_at":"2024-11-21T15:01:17Z","title":"Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T15:28:35.056169Z"},"links":{"citing_paper":"/paper/2411.14192"},"observation_digest":"sha256:7959ace0ca8aa1e9883a7edfd9915a37540cdcaaa44bb9e3e595e24c99204b2b","observation_id":"d3710e0d-3620-4ce3-95c5-2003ee9a29db","resolution":{"observed_at":"2026-08-12T15:28:36.207044Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T15:28:36.187696Z","title":"Carbon capture and storage: history and the road ahead","venue":null,"work_id":"3491cd33-62b3-4b64-a14b-c997c52cb991","year":2022},"citing_paper":{"arxiv_id":"2411.14192","last_updated":"2024-11-21T15:01:17Z","snapshot_observed_at":"2026-08-16T18:06:06.874752Z","submitted_at":"2024-11-21T15:01:17Z","title":"Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T15:28:35.156647Z"},"links":{"citing_paper":"/paper/2411.14192"},"observation_digest":"sha256:0893f69695778d7428a7a184ce0519dfe0f0cb1cf685f3a2f40d1f60d6209016","observation_id":"1df2ea5c-2244-4adb-84aa-4cdd4fd5ff8e","resolution":{"observed_at":"2026-08-12T15:28:36.192289Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T15:28:36.171077Z","title":"The role of carbon capture and storage to achieve net-zero energy systems: Trade-offs between economics and the environment","venue":null,"work_id":"a447ac18-7ff2-4790-b8e5-429af9480b96","year":2023},"citing_paper":{"arxiv_id":"2411.14192","last_updated":"2024-11-21T15:01:17Z","snapshot_observed_at":"2026-08-16T18:06:06.874752Z","submitted_at":"2024-11-21T15:01:17Z","title":"Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T15:28:35.160992Z"},"links":{"citing_paper":"/paper/2411.14192"},"observation_digest":"sha256:bcf0f6ec6751b65158a19798006d203a3e6ae17532e3b0f4e1acd45863050320","observation_id":"5ee04db5-bac9-48f5-a0bf-8e291d162f11","resolution":{"observed_at":"2026-08-12T15:28:36.176241Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T15:28:36.130019Z","title":"A review on underground hydrogen storage: Insight into geological sites, influencing factors and future outlook","venue":null,"work_id":"f10e6735-04e2-442e-92fe-fd6b59074027","year":2022},"citing_paper":{"arxiv_id":"2411.14192","last_updated":"2024-11-21T15:01:17Z","snapshot_observed_at":"2026-08-16T18:06:06.874752Z","submitted_at":"2024-11-21T15:01:17Z","title":"Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T15:28:35.165961Z"},"links":{"citing_paper":"/paper/2411.14192"},"observation_digest":"sha256:a518196c0618e94b058b08f3a0370038d1501117a0124769b23fd2d8d832dd85","observation_id":"8db2cbd6-f5c2-4930-9f43-68c28188deea","resolution":{"observed_at":"2026-08-12T15:28:36.160636Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T15:28:35.954135Z","title":"A review of cell-scale multiphase flow modeling, including water management, in polymer electrolyte fuel cells","venue":null,"work_id":"0618b25a-b756-4014-a43d-7bddf158fb63","year":2016},"citing_paper":{"arxiv_id":"2411.14192","last_updated":"2024-11-21T15:01:17Z","snapshot_observed_at":"2026-08-16T18:06:06.874752Z","submitted_at":"2024-11-21T15:01:17Z","title":"Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T15:28:35.171259Z"},"links":{"citing_paper":"/paper/2411.14192"},"observation_digest":"sha256:bf706dfaf0c2cda016d6a1a1a61b2f6779ef31016dfec3fc65afafcc2f3eedec","observation_id":"b4b746dd-2902-4511-bb15-fc2319049a03","resolution":{"observed_at":"2026-08-12T15:28:36.029790Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T15:28:35.928933Z","title":"Pore-scale imaging and modelling","venue":null,"work_id":"ade351c0-e183-448c-a760-69c425712034","year":2013},"citing_paper":{"arxiv_id":"2411.14192","last_updated":"2024-11-21T15:01:17Z","snapshot_observed_at":"2026-08-16T18:06:06.874752Z","submitted_at":"2024-11-21T15:01:17Z","title":"Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T15:28:35.176614Z"},"links":{"citing_paper":"/paper/2411.14192"},"observation_digest":"sha256:8d9f275ef9981f0b39a02c9e8afb5b5a8caeade2d5c0455722d85dc84ea6db6e","observation_id":"7a02862b-a4f3-4ebe-b167-f880511ca9b8","resolution":{"observed_at":"2026-08-12T15:28:35.933417Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T15:28:35.911473Z","title":"Real-time imaging reveals distinct pore-scale dynamics during transient and equilibrium subsurface multiphase flow","venue":null,"work_id":"88704441-2838-4e03-848e-f930185ef018","year":2020},"citing_paper":{"arxiv_id":"2411.14192","last_updated":"2024-11-21T15:01:17Z","snapshot_observed_at":"2026-08-16T18:06:06.874752Z","submitted_at":"2024-11-21T15:01:17Z","title":"Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T15:28:35.180657Z"},"links":{"citing_paper":"/paper/2411.14192"},"observation_digest":"sha256:e39e734e54c9f09e95f54a2af6eefd8b9ccf58b4b3c925d2140fb95a68d770a0","observation_id":"e68ab936-2b19-4afe-9ef8-458bd1b4c533","resolution":{"observed_at":"2026-08-12T15:28:35.917442Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T15:28:35.894425Z","title":"Compre- hensive comparison of pore-scale models for multiphase flow in porous media","venue":null,"work_id":"c4ee5393-c182-41dc-a4a8-d48bb849f2f9","year":2019},"citing_paper":{"arxiv_id":"2411.14192","last_updated":"2024-11-21T15:01:17Z","snapshot_observed_at":"2026-08-16T18:06:06.874752Z","submitted_at":"2024-11-21T15:01:17Z","title":"Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T15:28:35.184797Z"},"links":{"citing_paper":"/paper/2411.14192"},"observation_digest":"sha256:1982f25cefff8a403ac866be72f8bf967f7e7cec4f90160265c46ef34193d87f","observation_id":"c9941564-6d10-42e1-a4e8-ce95bca249b5","resolution":{"observed_at":"2026-08-12T15:28:35.899383Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T15:28:35.880192Z","title":"An intercomparison of the pore network to the navier–stokes modeling approach applied for saturated conductivity estimation from x-ray ct images","venue":null,"work_id":"00d11bc2-8a77-4a0f-ba70-1e79a1847543","year":2021},"citing_paper":{"arxiv_id":"2411.14192","last_updated":"2024-11-21T15:01:17Z","snapshot_observed_at":"2026-08-16T18:06:06.874752Z","submitted_at":"2024-11-21T15:01:17Z","title":"Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T15:28:35.189017Z"},"links":{"citing_paper":"/paper/2411.14192"},"observation_digest":"sha256:198cede5b93a43bddf32e39f692be6fca727e8f210d3a930437db663414c6b1b","observation_id":"863532a0-6054-4a86-a3b0-bf605f5694e7","resolution":{"observed_at":"2026-08-12T15:28:35.884914Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T15:28:35.864047Z","title":"Review of pore network modelling of porous media: Experimental characterisations, network constructions and applications to reactive transport","venue":null,"work_id":"8a538608-815a-4ee9-81a3-df4e87c0d5dc","year":2016},"citing_paper":{"arxiv_id":"2411.14192","last_updated":"2024-11-21T15:01:17Z","snapshot_observed_at":"2026-08-16T18:06:06.874752Z","submitted_at":"2024-11-21T15:01:17Z","title":"Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T15:28:35.193014Z"},"links":{"citing_paper":"/paper/2411.14192"},"observation_digest":"sha256:b21c7daae2ff367b9ef5334c76eedf9ad5d387ed541fc08f61e395a62f299e13","observation_id":"4f94779b-57e7-4a3c-b59f-978f0e80a631","resolution":{"observed_at":"2026-08-12T15:28:35.869391Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T15:28:35.779066Z","title":"Poreflow-net: A 3d convolutional neural network to predict fluid flow through porous media","venue":null,"work_id":"43e32335-d207-4536-bfd6-b9f35b4d6774","year":2020},"citing_paper":{"arxiv_id":"2411.14192","last_updated":"2024-11-21T15:01:17Z","snapshot_observed_at":"2026-08-16T18:06:06.874752Z","submitted_at":"2024-11-21T15:01:17Z","title":"Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T15:28:35.225672Z"},"links":{"citing_paper":"/paper/2411.14192"},"observation_digest":"sha256:3b2f9f30a865950770284f81304a18e31d7d95854414e3b62fe84ceb50e85014","observation_id":"2814afd6-fcf2-4dfd-a3fd-64148da22257","resolution":{"observed_at":"2026-08-12T15:28:35.839978Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T15:28:35.691191Z","title":"Neural network–based pore flow field prediction in porous media using super resolution","venue":null,"work_id":"24e19979-7a8c-4cd6-a981-475903d488a1","year":2022},"citing_paper":{"arxiv_id":"2411.14192","last_updated":"2024-11-21T15:01:17Z","snapshot_observed_at":"2026-08-16T18:06:06.874752Z","submitted_at":"2024-11-21T15:01:17Z","title":"Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T15:28:35.317518Z"},"links":{"citing_paper":"/paper/2411.14192"},"observation_digest":"sha256:e30bce5e01be6fc5ebcd2b69242f8cbd047fb07b2d327f1ec6ce02fdee8af384","observation_id":"f64bad84-3310-4e95-9968-46ec982114cf","resolution":{"observed_at":"2026-08-12T15:28:35.723167Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.03409","last_updated":"2021-06-18T16:32:43Z","snapshot_observed_at":"2026-08-16T19:15:13.406583Z","submitted_at":"2020-10-07T13:34:49Z","title":"Learning Mesh-Based Simulation with Graph Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.03409","snapshot_observed_at":"2026-08-12T15:28:35.389433Z","title":"Learning mesh-based simulation with graph networks","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2411.14192","last_updated":"2024-11-21T15:01:17Z","snapshot_observed_at":"2026-08-16T18:06:06.874752Z","submitted_at":"2024-11-21T15:01:17Z","title":"Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T15:28:35.389433Z"},"links":{"cited_paper":"/paper/2010.03409","citing_paper":"/paper/2411.14192"},"observation_digest":"sha256:a9d91b409153da4a9794a0ca8ede350cda6116785ed0e515710ad32d95b26f8c","observation_id":"9dd3a7d8-f54d-4ffb-a73f-bb0cafdc9d9a","resolution":{"observed_at":"2026-08-12T15:28:35.389433Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T15:28:35.393566Z","title":"Learning to simulate complex physics with graph networks","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.14192","last_updated":"2024-11-21T15:01:17Z","snapshot_observed_at":"2026-08-16T18:06:06.874752Z","submitted_at":"2024-11-21T15:01:17Z","title":"Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T15:28:35.393566Z"},"links":{"citing_paper":"/paper/2411.14192"},"observation_digest":"sha256:fe19ab5dc29f354c2875091df54c83115e99050e7b7f44980ffbd58de30f1be1","observation_id":"6375d6ad-1c77-4790-b1cb-306d5dc47dc7","resolution":{"observed_at":"2026-08-12T15:28:35.393566Z","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":"2026-08-12T15:28:35.666759Z","title":"Learning large-scale subsurface simulations with a hybrid graph network simulator","venue":null,"work_id":"e4f78aee-33dd-467d-85bb-5490cb46c540","year":2022},"citing_paper":{"arxiv_id":"2411.14192","last_updated":"2024-11-21T15:01:17Z","snapshot_observed_at":"2026-08-16T18:06:06.874752Z","submitted_at":"2024-11-21T15:01:17Z","title":"Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T15:28:35.398002Z"},"links":{"citing_paper":"/paper/2411.14192"},"observation_digest":"sha256:71502a2432c5cf3a49b3af4c295ff791996ae40e9b891bba543747d93e4c3f5f","observation_id":"076cd9cc-6156-4bd5-96a6-f58befded042","resolution":{"observed_at":"2026-08-12T15:28:35.671691Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T15:28:35.651548Z","title":"Python workflow for segmenting multiphase flow in porous rocks","venue":null,"work_id":"aba9fb75-41b9-4a58-9b1b-d668cf49b01c","year":2023},"citing_paper":{"arxiv_id":"2411.14192","last_updated":"2024-11-21T15:01:17Z","snapshot_observed_at":"2026-08-16T18:06:06.874752Z","submitted_at":"2024-11-21T15:01:17Z","title":"Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T15:28:35.402274Z"},"links":{"citing_paper":"/paper/2411.14192"},"observation_digest":"sha256:4c087a4c9dbfdea96d7bad6f5dfbe1e39bac685e63265f25c3827550e68dbe85","observation_id":"e849fef5-6bd0-4f7d-8a0b-17e0eb08c6e5","resolution":{"observed_at":"2026-08-12T15:28:35.656361Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.00612","last_updated":"2022-10-02T20:16:20Z","snapshot_observed_at":"2026-08-16T16:27:28.262613Z","submitted_at":"2022-10-02T20:16:20Z","title":"MultiScale MeshGraphNets","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.00612","snapshot_observed_at":"2026-08-12T15:28:35.407803Z","title":"Multiscale meshgraphnets","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.14192","last_updated":"2024-11-21T15:01:17Z","snapshot_observed_at":"2026-08-16T18:06:06.874752Z","submitted_at":"2024-11-21T15:01:17Z","title":"Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T15:28:35.407803Z"},"links":{"cited_paper":"/paper/2210.00612","citing_paper":"/paper/2411.14192"},"observation_digest":"sha256:81f67bc6599dba75b5a4fe4369e57657241d8bdb48dc7881d1712b7140313a46","observation_id":"2189579b-2849-4f13-9e66-091908c5cf33","resolution":{"observed_at":"2026-08-12T15:28:35.407803Z","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":"2026-08-12T15:28:35.633925Z","title":"On the optimal combi- nation of cross-entropy and soft dice losses for lesion segmentation with out-of-distribution robustness","venue":null,"work_id":"b5a93117-d27a-4880-a544-cb8b7f55f8b0","year":2022},"citing_paper":{"arxiv_id":"2411.14192","last_updated":"2024-11-21T15:01:17Z","snapshot_observed_at":"2026-08-16T18:06:06.874752Z","submitted_at":"2024-11-21T15:01:17Z","title":"Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T15:28:35.412882Z"},"links":{"citing_paper":"/paper/2411.14192"},"observation_digest":"sha256:155f49cda39d6070a98987ead0b139c1f90bd4f06e86af68972a4e44f67fb33f","observation_id":"6106a40f-45ae-4737-91e0-1d7d26034884","resolution":{"observed_at":"2026-08-12T15:28:35.639886Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1908.03679","last_updated":"2019-08-10T03:37:18Z","snapshot_observed_at":"2026-08-18T07:25:30.374497Z","submitted_at":"2019-08-10T03:37:18Z","title":"Distance Map Loss Penalty Term for Semantic Segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1908.03679","snapshot_observed_at":"2026-08-12T15:28:35.416971Z","title":"Distance map loss penalty term for semantic segmentation","venue":null,"work_id":null,"year":1908},"citing_paper":{"arxiv_id":"2411.14192","last_updated":"2024-11-21T15:01:17Z","snapshot_observed_at":"2026-08-16T18:06:06.874752Z","submitted_at":"2024-11-21T15:01:17Z","title":"Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T15:28:35.416971Z"},"links":{"cited_paper":"/paper/1908.03679","citing_paper":"/paper/2411.14192"},"observation_digest":"sha256:7dc964cbbb2c0ff69602c2225e39bc5fb31f187f399e62ac26283455a3c8310b","observation_id":"e79308cd-b293-4ef1-a847-af99e0e76a4f","resolution":{"observed_at":"2026-08-12T15:28:35.416971Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T15:28:35.421349Z","title":"Focal loss for dense object detection","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.14192","last_updated":"2024-11-21T15:01:17Z","snapshot_observed_at":"2026-08-16T18:06:06.874752Z","submitted_at":"2024-11-21T15:01:17Z","title":"Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T15:28:35.421349Z"},"links":{"citing_paper":"/paper/2411.14192"},"observation_digest":"sha256:a6ba5f88c7cc804eaa265d9aa2fb691d0ea93e8a04efdc6b5c8f86bc69cbb3de","observation_id":"284a30d3-3e7d-4da4-a6b8-32285b6cd68b","resolution":{"observed_at":"2026-08-12T15:28:35.421349Z","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":"2026-08-12T15:28:35.582759Z","title":"Tversky loss function for image segmentation using 3d fully convolutional deep networks","venue":null,"work_id":"77ccc186-dc23-44c4-b065-625cc5ada6dd","year":2017},"citing_paper":{"arxiv_id":"2411.14192","last_updated":"2024-11-21T15:01:17Z","snapshot_observed_at":"2026-08-16T18:06:06.874752Z","submitted_at":"2024-11-21T15:01:17Z","title":"Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T15:28:35.426260Z"},"links":{"citing_paper":"/paper/2411.14192"},"observation_digest":"sha256:0a5e152e65c0cb6f245eb0cfc8f6e78ce26f4e21a067940edbd695f181731f65","observation_id":"cdf774f7-dcc7-42f5-98bf-f0ae77551bfc","resolution":{"observed_at":"2026-08-12T15:28:35.613500Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2411.14192","last_updated":"2024-11-21T15:01:17Z","latest_version":1,"primary_category":"physics.flu-dyn","snapshot_observed_at":"2026-08-16T18:06:06.874752Z","submitted_at":"2024-11-21T15:01:17Z","title":"Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network"},"reference_resolution":{"displayed":22,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":6,"verified_exact":0,"verified_fuzzy":16},"total_outbound_references":22},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2411.14192."}