{"as_of":"2026-08-10T01:45:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:dd7aac298519c75dd89fcf54322c4d311dca3c2701f3ed5de987e0d148c48305","coverage":[{"denominator":21,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":21,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-02T12:44:53.156757Z","state":"measured"},{"denominator":21,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":21,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+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/2607.20480/citation-record","integrity":"/paper/2607.20480/integrity","json":"/paper/2607.20480/citation-record.json","paper":"/paper/2607.20480"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T12:44:51.400645Z","title":"Distribution grid impedance & topology estimation with limited or no micro-pmus,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.20480","last_updated":"2026-05-30T06:00:34Z","snapshot_observed_at":"2026-08-09T23:36:25.683666Z","submitted_at":"2026-05-30T06:00:34Z","title":"Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-02T12:44:51.400645Z"},"links":{"citing_paper":"/paper/2607.20480"},"observation_digest":"sha256:fbaf9c54f65bdd9574601802fbad4ef5b4dd39507aacb65fcff5d1dcbfeb32ff","observation_id":"7fd42812-9f5a-40bd-a569-73ef1ee24bae","resolution":{"observed_at":"2026-08-02T12:44:51.400645Z","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":"2026-08-02T12:44:51.484456Z","title":"Hd-deep-em: Deep expectation maximization for dynamic hidden state recovery using heterogeneous data,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.20480","last_updated":"2026-05-30T06:00:34Z","snapshot_observed_at":"2026-08-09T23:36:25.683666Z","submitted_at":"2026-05-30T06:00:34Z","title":"Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-02T12:44:51.484456Z"},"links":{"citing_paper":"/paper/2607.20480"},"observation_digest":"sha256:d1e1c5eef2cb2c9971d254d427ef9bd08a269e8592451e0d0c0901ea3d2e623c","observation_id":"b84d4743-764f-4c28-bc8c-cb3b87ae3f9d","resolution":{"observed_at":"2026-08-02T12:44:51.484456Z","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":"2026-08-02T12:44:51.577100Z","title":"Distributed algorithms for convexified bad data and topology error detection and identification problems,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.20480","last_updated":"2026-05-30T06:00:34Z","snapshot_observed_at":"2026-08-09T23:36:25.683666Z","submitted_at":"2026-05-30T06:00:34Z","title":"Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-02T12:44:51.577100Z"},"links":{"citing_paper":"/paper/2607.20480"},"observation_digest":"sha256:6733313c9dfc79e4a459991072aeef813d0d1d7ef8ae415ef325aae5debdb469","observation_id":"256752a2-1330-4059-a1fa-c8ee25a5f0a7","resolution":{"observed_at":"2026-08-02T12:44:51.577100Z","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":"2026-08-02T12:44:51.726018Z","title":"Machine learning-enabled distribution network phase identification,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.20480","last_updated":"2026-05-30T06:00:34Z","snapshot_observed_at":"2026-08-09T23:36:25.683666Z","submitted_at":"2026-05-30T06:00:34Z","title":"Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-02T12:44:51.726018Z"},"links":{"citing_paper":"/paper/2607.20480"},"observation_digest":"sha256:8994826ed46fcaf9afcfa0ed063767f22f5e1ff0f0cc78820f66a1487f9e0480","observation_id":"1148d80b-13a2-4976-ab94-8345cc237b5e","resolution":{"observed_at":"2026-08-02T12:44:51.726018Z","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":"2026-08-02T12:44:51.798806Z","title":"Guaranteed con- version from static measurements into dynamic ones based on manifold feature interpolation,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.20480","last_updated":"2026-05-30T06:00:34Z","snapshot_observed_at":"2026-08-09T23:36:25.683666Z","submitted_at":"2026-05-30T06:00:34Z","title":"Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-02T12:44:51.798806Z"},"links":{"citing_paper":"/paper/2607.20480"},"observation_digest":"sha256:12f2cad1d73a07c289e55f70a3cfa1ac102f86988e5145989902ab86e7912aab","observation_id":"677092be-1034-4f86-8103-8fa06f51f8d5","resolution":{"observed_at":"2026-08-02T12:44:51.798806Z","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":"2026-08-02T12:44:51.907958Z","title":"Tajer, S","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.20480","last_updated":"2026-05-30T06:00:34Z","snapshot_observed_at":"2026-08-09T23:36:25.683666Z","submitted_at":"2026-05-30T06:00:34Z","title":"Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-02T12:44:51.907958Z"},"links":{"citing_paper":"/paper/2607.20480"},"observation_digest":"sha256:b75ebd3922c24449dd21320c1614e4b0e6b54c42f1f36d6212197ab00b4093ec","observation_id":"73fc12a4-9aea-4a26-954d-abdc360a5949","resolution":{"observed_at":"2026-08-02T12:44:51.907958Z","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":"2026-08-02T12:44:51.995900Z","title":"Efficient manifold-constrained neural ode for high-dimensional datasets,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.20480","last_updated":"2026-05-30T06:00:34Z","snapshot_observed_at":"2026-08-09T23:36:25.683666Z","submitted_at":"2026-05-30T06:00:34Z","title":"Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-02T12:44:51.995900Z"},"links":{"citing_paper":"/paper/2607.20480"},"observation_digest":"sha256:523cfa94dccd2586a46a3b2f70099524ec77417fd2d418e88d3f5f9dc9eae76a","observation_id":"0aa0c90a-e449-4174-b728-39c7841d726f","resolution":{"observed_at":"2026-08-02T12:44:51.995900Z","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":"2026-08-02T12:44:52.098757Z","title":"Graph mining for classifying and localizing solar panels in distribution grids,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.20480","last_updated":"2026-05-30T06:00:34Z","snapshot_observed_at":"2026-08-09T23:36:25.683666Z","submitted_at":"2026-05-30T06:00:34Z","title":"Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-02T12:44:52.098757Z"},"links":{"citing_paper":"/paper/2607.20480"},"observation_digest":"sha256:2ca9df5e9ec2822f6cf86b548e46a08c40f819475a5ea3a60d58739d59091766","observation_id":"91713f5c-a7ac-4303-9c33-eedc5885a999","resolution":{"observed_at":"2026-08-02T12:44:52.098757Z","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":"2026-08-02T12:44:52.172848Z","title":"Identifying errors in service transformer connections,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.20480","last_updated":"2026-05-30T06:00:34Z","snapshot_observed_at":"2026-08-09T23:36:25.683666Z","submitted_at":"2026-05-30T06:00:34Z","title":"Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-02T12:44:52.172848Z"},"links":{"citing_paper":"/paper/2607.20480"},"observation_digest":"sha256:cb620216faf439237b77d0b72a02c0a1486bd80444fb805eae31b9dc84114f59","observation_id":"12339069-e8a2-4b52-ab9d-d3db771f71dc","resolution":{"observed_at":"2026-08-02T12:44:52.172848Z","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":"2026-08-02T12:44:52.240677Z","title":"Core process representation in power system operational models: Gaps, challenges, and opportunities for multisector dynamics research,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.20480","last_updated":"2026-05-30T06:00:34Z","snapshot_observed_at":"2026-08-09T23:36:25.683666Z","submitted_at":"2026-05-30T06:00:34Z","title":"Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-02T12:44:52.240677Z"},"links":{"citing_paper":"/paper/2607.20480"},"observation_digest":"sha256:26fb5d0f91af5cd37b14b21d10f5870d2cb0d27b83204fef76aac5d85857359b","observation_id":"33084421-f688-4e7e-ab08-735228ae5d3c","resolution":{"observed_at":"2026-08-02T12:44:52.240677Z","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":"2026-08-02T12:44:52.351345Z","title":"Data quality challenges in existing distribution network datasets,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.20480","last_updated":"2026-05-30T06:00:34Z","snapshot_observed_at":"2026-08-09T23:36:25.683666Z","submitted_at":"2026-05-30T06:00:34Z","title":"Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-02T12:44:52.351345Z"},"links":{"citing_paper":"/paper/2607.20480"},"observation_digest":"sha256:3b13b94d0a94156e1390e6e0c6385189bd6e2a06c2c3f826cbe7fd1d15f6209a","observation_id":"4afdb540-bdff-4961-9ddc-9ea884eaf9b4","resolution":{"observed_at":"2026-08-02T12:44:52.351345Z","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":"2026-08-02T12:44:52.409518Z","title":"An efficient approach to power system uncertainty analysis with high-dimensional dependencies,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.20480","last_updated":"2026-05-30T06:00:34Z","snapshot_observed_at":"2026-08-09T23:36:25.683666Z","submitted_at":"2026-05-30T06:00:34Z","title":"Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-02T12:44:52.409518Z"},"links":{"citing_paper":"/paper/2607.20480"},"observation_digest":"sha256:a24f29fb7e472930f9e29731b04a08c73103a2d6caeaa8122df262b7e5f0221b","observation_id":"f78dd6b2-a892-46a9-aa35-4d3b0b84416c","resolution":{"observed_at":"2026-08-02T12:44:52.409518Z","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":"2026-08-02T12:44:52.480166Z","title":"Spatial-temporal deep learning for hosting capacity analysis in distribution grids,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.20480","last_updated":"2026-05-30T06:00:34Z","snapshot_observed_at":"2026-08-09T23:36:25.683666Z","submitted_at":"2026-05-30T06:00:34Z","title":"Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-02T12:44:52.480166Z"},"links":{"citing_paper":"/paper/2607.20480"},"observation_digest":"sha256:1a9e27d0a45efe2ed999d532e4807129da6ba866aa44bf526fcecfcf0753c181","observation_id":"100baadd-368c-493a-9c88-a9416173a783","resolution":{"observed_at":"2026-08-02T12:44:52.480166Z","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":"2026-08-02T12:44:52.553509Z","title":"Solar photovoltaic assessment with large lan- guage model,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.20480","last_updated":"2026-05-30T06:00:34Z","snapshot_observed_at":"2026-08-09T23:36:25.683666Z","submitted_at":"2026-05-30T06:00:34Z","title":"Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-02T12:44:52.553509Z"},"links":{"citing_paper":"/paper/2607.20480"},"observation_digest":"sha256:e8404e8fe9581b0e152e438762ac3dd60ebab868103e8d8a76e993724915cffd","observation_id":"c0f7b257-fbb5-48d6-b694-c8d34206c7ec","resolution":{"observed_at":"2026-08-02T12:44:52.553509Z","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":"2026-08-02T12:44:52.659482Z","title":"Topology identification and line parameter estimation for non-pmu distribution network: A numerical method,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.20480","last_updated":"2026-05-30T06:00:34Z","snapshot_observed_at":"2026-08-09T23:36:25.683666Z","submitted_at":"2026-05-30T06:00:34Z","title":"Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-02T12:44:52.659482Z"},"links":{"citing_paper":"/paper/2607.20480"},"observation_digest":"sha256:b011184fba1e3a0eb2431b3a9e358df52fe5b4207ade68604831e90cd3878ef1","observation_id":"d88878b5-335f-4929-94ee-6c7ce6fb25ab","resolution":{"observed_at":"2026-08-02T12:44:52.659482Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.17488","last_updated":"2025-05-23T05:28:59Z","snapshot_observed_at":"2026-08-09T05:15:09.782577Z","submitted_at":"2025-05-23T05:28:59Z","title":"ExARNN: An Environment-Driven Adaptive RNN for Learning Non-Stationary Power Dynamics","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.17488","snapshot_observed_at":"2026-08-02T12:44:52.733395Z","title":"Exarnn: An environment-driven adaptive rnn for learning non-stationary power dy- namics,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.20480","last_updated":"2026-05-30T06:00:34Z","snapshot_observed_at":"2026-08-09T23:36:25.683666Z","submitted_at":"2026-05-30T06:00:34Z","title":"Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-02T12:44:52.733395Z"},"links":{"cited_paper":"/paper/2505.17488","citing_paper":"/paper/2607.20480"},"observation_digest":"sha256:697be83dcec45955b069f15bc6bab190113cc60349d8fb9f35298a1b9804c0f0","observation_id":"e0b988be-f85c-47ec-b1d4-97562f49740d","resolution":{"observed_at":"2026-08-02T12:44:52.733395Z","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":"2026-08-02T12:44:52.799330Z","title":"Phase identification in electric power distribution systems by clustering of smart meter data,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.20480","last_updated":"2026-05-30T06:00:34Z","snapshot_observed_at":"2026-08-09T23:36:25.683666Z","submitted_at":"2026-05-30T06:00:34Z","title":"Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-02T12:44:52.799330Z"},"links":{"citing_paper":"/paper/2607.20480"},"observation_digest":"sha256:ee95a9a1fff515a585cf7f3323bd0fb7ca482e79d30d6f02525869119d1bb8f5","observation_id":"c9e181e6-3ad7-4b44-b783-77381e658e56","resolution":{"observed_at":"2026-08-02T12:44:52.799330Z","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":"2026-08-02T12:44:52.904367Z","title":"An introduction to optimal power flow: Theory, formulation, and examples,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.20480","last_updated":"2026-05-30T06:00:34Z","snapshot_observed_at":"2026-08-09T23:36:25.683666Z","submitted_at":"2026-05-30T06:00:34Z","title":"Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-02T12:44:52.904367Z"},"links":{"citing_paper":"/paper/2607.20480"},"observation_digest":"sha256:ba68222c8870af2d449e7eb9e65eeb1ec4e900ad614ad4b563d399d0a6993289","observation_id":"570ccfa4-e7ae-4c56-967f-ec56fc769321","resolution":{"observed_at":"2026-08-02T12:44:52.904367Z","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":"2026-08-02T12:44:52.979476Z","title":"Physical equation discovery using physics- consistent neural network (pcnn) under incomplete observability,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.20480","last_updated":"2026-05-30T06:00:34Z","snapshot_observed_at":"2026-08-09T23:36:25.683666Z","submitted_at":"2026-05-30T06:00:34Z","title":"Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-02T12:44:52.979476Z"},"links":{"citing_paper":"/paper/2607.20480"},"observation_digest":"sha256:b9139f335d40073d69d280073d27a2bc58156ed69f9ea80f23c961d49c890c6f","observation_id":"59716f27-385f-48e8-9244-ca9592825ac5","resolution":{"observed_at":"2026-08-02T12:44:52.979476Z","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":"2026-08-02T12:44:53.050730Z","title":"Adaptive data fusion for state estimation and control of power grids under attack,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.20480","last_updated":"2026-05-30T06:00:34Z","snapshot_observed_at":"2026-08-09T23:36:25.683666Z","submitted_at":"2026-05-30T06:00:34Z","title":"Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-02T12:44:53.050730Z"},"links":{"citing_paper":"/paper/2607.20480"},"observation_digest":"sha256:b4d24179605af81572b329dd0491da9a2093f26572c43a431e1fec53d1c30fc4","observation_id":"f12d3f18-0e38-4c44-880a-6f0e23656ba1","resolution":{"observed_at":"2026-08-02T12:44:53.050730Z","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":"2026-08-02T12:44:53.156757Z","title":"A joint estimation method of distribution network topology and line parameters based on power flow graph convolutional networks,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.20480","last_updated":"2026-05-30T06:00:34Z","snapshot_observed_at":"2026-08-09T23:36:25.683666Z","submitted_at":"2026-05-30T06:00:34Z","title":"Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-02T12:44:53.156757Z"},"links":{"citing_paper":"/paper/2607.20480"},"observation_digest":"sha256:72e2b444cb07e9e465c9371920344640fa79eb2f9246b338e2bc61eb5a28d33b","observation_id":"8f1f69d5-43d2-4e84-b448-b348ae851f80","resolution":{"observed_at":"2026-08-02T12:44:53.156757Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.20480","last_updated":"2026-05-30T06:00:34Z","latest_version":1,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-09T23:36:25.683666Z","submitted_at":"2026-05-30T06:00:34Z","title":"Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference"},"reference_resolution":{"displayed":21,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":21,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":21},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2607.20480."}