{"as_of":"2026-08-10T15:41:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:fdf42cfd80351f0e0a760cc2fe735795c30c1f5584a7b8a8a6fa0ee1f907650b","coverage":[{"denominator":38,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":38,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T17:25:44.618938Z","state":"measured"},{"denominator":39,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":39,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-10T15:20:31.017699Z","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":"2026-05-11T10:46:02.378512Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2507.11574","last_updated":"2025-07-15T04:26:40Z","snapshot_observed_at":"2026-08-09T08:21:42.701137Z","submitted_at":"2025-07-15T04:26:40Z","title":"Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators","version":1},"cited_work":{"arxiv_id":"2507.11574","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2507.11574","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"22c161db-a812-4e2f-90a8-5da0fb48a613","year":2025},"citing_paper":{"arxiv_id":"2604.13316","last_updated":"2026-04-14T21:43:09Z","snapshot_observed_at":"2026-08-06T21:22:23.205962Z","submitted_at":"2026-04-14T21:43:09Z","title":"Beyond Uniform Sampling: Synergistic Active Learning and Input Denoising for Robust Neural Operators","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-05-10T15:20:31.017699Z"},"links":{"cited_paper":"/paper/2507.11574","citing_paper":"/paper/2604.13316"},"observation_digest":"sha256:1b78668571ca46f92d95e55da723f6b5f18bc3dd37efc946cebfe0d75e72f7ad","observation_id":"5a0a84e3-77b4-408b-b0e6-906e4c7c196f","resolution":{"observed_at":"2026-05-11T10:46:02.381557Z","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/2507.11574/citation-record","integrity":"/paper/2507.11574/integrity","json":"/paper/2507.11574/citation-record.json","paper":"/paper/2507.11574"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:25:49.969874Z","title":null,"venue":null,"work_id":"9e2f2888-1af5-4875-965e-4b7fc4c81489","year":2024},"citing_paper":{"arxiv_id":"2507.11574","last_updated":"2025-07-15T04:26:40Z","snapshot_observed_at":"2026-08-09T08:21:42.701137Z","submitted_at":"2025-07-15T04:26:40Z","title":"Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T17:25:42.005069Z"},"links":{"citing_paper":"/paper/2507.11574"},"observation_digest":"sha256:9e657a570d54014ab7b75e9f687a2760a5931281c8ef22aae97b4b069251c56b","observation_id":"4b4addca-317e-418b-b13b-bac561d34a4c","resolution":{"observed_at":"2026-08-06T17:25:50.033195Z","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":{"arxiv_id":"2506.12045","last_updated":"2025-05-24T16:24:10Z","snapshot_observed_at":"2026-08-07T14:23:39.393302Z","submitted_at":"2025-05-24T16:24:10Z","title":"From Proxies to Fields: Spatiotemporal Reconstruction of Global Radiation from Sparse Sensor Sequences","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.12045","snapshot_observed_at":"2026-08-06T17:25:42.066252Z","title":"From proxies to fields: Spatiotemporal reconstruction of global radiation from sparse sensor sequences","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.11574","last_updated":"2025-07-15T04:26:40Z","snapshot_observed_at":"2026-08-09T08:21:42.701137Z","submitted_at":"2025-07-15T04:26:40Z","title":"Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T17:25:42.066252Z"},"links":{"cited_paper":"/paper/2506.12045","citing_paper":"/paper/2507.11574"},"observation_digest":"sha256:07ebaaee1f304969388d80f2f5aa8d247f2e9da8c0de71ed598a2a62bda1f011","observation_id":"e1d9a6ea-c28c-47c1-8d94-4607417ce2b1","resolution":{"observed_at":"2026-08-06T17:25:42.066252Z","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":"2026-08-06T17:25:42.137757Z","title":"Virtual sensing to enable real-time monitoring of inaccessible locations & unmeasurable parameters","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.11574","last_updated":"2025-07-15T04:26:40Z","snapshot_observed_at":"2026-08-09T08:21:42.701137Z","submitted_at":"2025-07-15T04:26:40Z","title":"Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T17:25:42.137757Z"},"links":{"citing_paper":"/paper/2507.11574"},"observation_digest":"sha256:d1eda477817378840307e045f12eaee2dcd8b078d24723cab6fa2a8c55686caf","observation_id":"17499541-1319-49ca-afc7-9903722920b0","resolution":{"observed_at":"2026-08-06T17:25:42.137757Z","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":"2026-08-06T17:25:49.768973Z","title":"Virtual sensing-enabled digital twin framework for real-time monitoring of nuclear systems leveraging deep neural operators","venue":null,"work_id":"6042ba2e-9b14-4cc7-b1f4-6a09ec7b7f0d","year":2025},"citing_paper":{"arxiv_id":"2507.11574","last_updated":"2025-07-15T04:26:40Z","snapshot_observed_at":"2026-08-09T08:21:42.701137Z","submitted_at":"2025-07-15T04:26:40Z","title":"Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T17:25:42.171223Z"},"links":{"citing_paper":"/paper/2507.11574"},"observation_digest":"sha256:34ec321ccdad725475c95d9bfdf24258f79d8f377b2576af63f2f529cc429bd4","observation_id":"830f564a-9a21-4dac-b310-e267f08d1d60","resolution":{"observed_at":"2026-08-06T17:25:49.864413Z","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":"2026-08-06T17:25:49.568087Z","title":"Deep neural operator-driven real-time inference to enable digital twin solutions for nuclear energy systems","venue":null,"work_id":"e6aaa1f0-342f-41c0-8d8f-634070777f8b","year":2024},"citing_paper":{"arxiv_id":"2507.11574","last_updated":"2025-07-15T04:26:40Z","snapshot_observed_at":"2026-08-09T08:21:42.701137Z","submitted_at":"2025-07-15T04:26:40Z","title":"Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T17:25:42.268733Z"},"links":{"citing_paper":"/paper/2507.11574"},"observation_digest":"sha256:aa17361d43849551e99cce96012c53949c31091ad23f7abe7f6a14dd6b3c42ef","observation_id":"0781e987-a335-48f9-ab70-032440cd590d","resolution":{"observed_at":"2026-08-06T17:25:49.645389Z","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":"2010.08895","last_updated":"2021-05-17T03:12:33Z","snapshot_observed_at":"2026-07-06T10:05:26.653366Z","submitted_at":"2020-10-18T00:34:21Z","title":"Fourier Neural Operator for Parametric Partial Differential Equations","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.08895","snapshot_observed_at":"2026-08-06T17:25:42.348296Z","title":"Fourier neural operator for parametric partial differential equations","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2507.11574","last_updated":"2025-07-15T04:26:40Z","snapshot_observed_at":"2026-08-09T08:21:42.701137Z","submitted_at":"2025-07-15T04:26:40Z","title":"Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T17:25:42.348296Z"},"links":{"cited_paper":"/paper/2010.08895","citing_paper":"/paper/2507.11574"},"observation_digest":"sha256:c83bbfef62516741adce76fe6be75ea03897fc38299d898446036a4287bfc22e","observation_id":"0ceec502-5548-4b42-803b-89f06d0ca227","resolution":{"observed_at":"2026-08-06T17:25:42.348296Z","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":"2026-08-06T17:25:49.483166Z","title":"Spherical fourier neural operators: Learning stable dynamics on the sphere","venue":null,"work_id":"130e228d-730e-4b81-a2db-1f55469055fe","year":2023},"citing_paper":{"arxiv_id":"2507.11574","last_updated":"2025-07-15T04:26:40Z","snapshot_observed_at":"2026-08-09T08:21:42.701137Z","submitted_at":"2025-07-15T04:26:40Z","title":"Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T17:25:42.461113Z"},"links":{"citing_paper":"/paper/2507.11574"},"observation_digest":"sha256:215f4b1a6c35e9cee4c43005ec13558b160a3f516bb4586b659096b1a951309f","observation_id":"a40fd036-cce9-4260-9076-d0b2b93c3168","resolution":{"observed_at":"2026-08-06T17:25:49.526137Z","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":"2003.03485","last_updated":"2020-03-07T01:56:20Z","snapshot_observed_at":"2026-08-03T02:26:02.483819Z","submitted_at":"2020-03-07T01:56:20Z","title":"Neural Operator: Graph Kernel Network for Partial Differential Equations","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.03485","snapshot_observed_at":"2026-08-06T17:25:42.532107Z","title":"Neural operator: Graph kernel network for partial differential equations","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2507.11574","last_updated":"2025-07-15T04:26:40Z","snapshot_observed_at":"2026-08-09T08:21:42.701137Z","submitted_at":"2025-07-15T04:26:40Z","title":"Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T17:25:42.532107Z"},"links":{"cited_paper":"/paper/2003.03485","citing_paper":"/paper/2507.11574"},"observation_digest":"sha256:bc52ea164eb7fd24db85a9f344f42bc9d34550e48334224e722542c96564b841","observation_id":"96c14388-33a4-4bc5-83b0-61cf24f0c2ac","resolution":{"observed_at":"2026-08-06T17:25:42.532107Z","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":"2026-08-06T17:25:42.636924Z","title":"Multipole graph neural operator for parametric partial differential equations","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.11574","last_updated":"2025-07-15T04:26:40Z","snapshot_observed_at":"2026-08-09T08:21:42.701137Z","submitted_at":"2025-07-15T04:26:40Z","title":"Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T17:25:42.636924Z"},"links":{"citing_paper":"/paper/2507.11574"},"observation_digest":"sha256:8c00d36ad380571a245bf07deb6c45e95d7d1efbb2b1cf8bfe22cf11918ecb23","observation_id":"9405c482-5bf8-4659-b0d8-877615380592","resolution":{"observed_at":"2026-08-06T17:25:42.636924Z","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":"2026-08-06T17:25:42.723427Z","title":"Wavelet neural operator for solving parametric partial differential equations in computational mechanics problems","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.11574","last_updated":"2025-07-15T04:26:40Z","snapshot_observed_at":"2026-08-09T08:21:42.701137Z","submitted_at":"2025-07-15T04:26:40Z","title":"Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T17:25:42.723427Z"},"links":{"citing_paper":"/paper/2507.11574"},"observation_digest":"sha256:258a80e0d06076853313cf4366b818f9776232ba847de701420ad7eab78f9185","observation_id":"186df835-fd43-4f26-bdee-7b94d60032ec","resolution":{"observed_at":"2026-08-06T17:25:42.723427Z","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":"2026-08-06T17:25:49.345329Z","title":"A wavelet neural operator based elastography for localization and quantification of tumors","venue":null,"work_id":"a712265e-4113-43cb-ba19-40848e4901e2","year":2023},"citing_paper":{"arxiv_id":"2507.11574","last_updated":"2025-07-15T04:26:40Z","snapshot_observed_at":"2026-08-09T08:21:42.701137Z","submitted_at":"2025-07-15T04:26:40Z","title":"Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T17:25:42.801124Z"},"links":{"citing_paper":"/paper/2507.11574"},"observation_digest":"sha256:62b23de5a8a749acd3b2a19272ff1ba63b9a5d733e38bd8a0a26ff8f1575aad0","observation_id":"b2d3d4e9-d706-4544-9f49-ffdb68267473","resolution":{"observed_at":"2026-08-06T17:25:49.397246Z","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":"2026-08-06T17:25:42.880168Z","title":"Learning nonlinear operators via deeponet based on the universal approximation theorem of operators","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.11574","last_updated":"2025-07-15T04:26:40Z","snapshot_observed_at":"2026-08-09T08:21:42.701137Z","submitted_at":"2025-07-15T04:26:40Z","title":"Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T17:25:42.880168Z"},"links":{"citing_paper":"/paper/2507.11574"},"observation_digest":"sha256:9d52067a773aa9f40763628c6854ec7eaaf47cd5510c9954f689f817694407db","observation_id":"f0bb523c-6421-44cf-b6ca-b05ca8aecf53","resolution":{"observed_at":"2026-08-06T17:25:42.880168Z","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":"2026-08-06T17:25:42.980779Z","title":null,"venue":null,"work_id":null,"year":1995},"citing_paper":{"arxiv_id":"2507.11574","last_updated":"2025-07-15T04:26:40Z","snapshot_observed_at":"2026-08-09T08:21:42.701137Z","submitted_at":"2025-07-15T04:26:40Z","title":"Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T17:25:42.980779Z"},"links":{"citing_paper":"/paper/2507.11574"},"observation_digest":"sha256:a9a1126a88d908461198faf886a40ccd212f8b6588975e4db2a901b963bc2686","observation_id":"4a606847-1a75-4f43-bee7-11330c1bddf5","resolution":{"observed_at":"2026-08-06T17:25:42.980779Z","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":"2026-08-06T17:25:43.093566Z","title":"Mionet: Learning multiple-input operators via tensor product","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.11574","last_updated":"2025-07-15T04:26:40Z","snapshot_observed_at":"2026-08-09T08:21:42.701137Z","submitted_at":"2025-07-15T04:26:40Z","title":"Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T17:25:43.093566Z"},"links":{"citing_paper":"/paper/2507.11574"},"observation_digest":"sha256:a39e689b8a1e2e9e19207603e9705ee9c0ab06dd3a27ca7bb2ff608ba8f82d0f","observation_id":"1e7745e0-1d6a-4f4f-b703-c801c3934944","resolution":{"observed_at":"2026-08-06T17:25:43.093566Z","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":"2026-08-06T17:25:49.134326Z","title":"Sequential deep operator networks (s-deeponet) for predicting full-field solutions under time-dependent loads","venue":null,"work_id":"dcb24fa5-6a98-4a51-8bc1-5a7e4271a2c8","year":2024},"citing_paper":{"arxiv_id":"2507.11574","last_updated":"2025-07-15T04:26:40Z","snapshot_observed_at":"2026-08-09T08:21:42.701137Z","submitted_at":"2025-07-15T04:26:40Z","title":"Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T17:25:43.174128Z"},"links":{"citing_paper":"/paper/2507.11574"},"observation_digest":"sha256:5939f05c69245356a4cd21132f548dbb4aa5a5c8b3147caf6ccc8c7f48f600b4","observation_id":"f0926038-72ff-4cf1-9014-235fa9270bc4","resolution":{"observed_at":"2026-08-06T17:25:49.250368Z","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":"2026-08-06T17:25:48.992404Z","title":"Predictions of transient vector solution fields with sequential deep operator network","venue":null,"work_id":"c5ab7dc0-ff28-412f-8598-be303fbd2a3f","year":2024},"citing_paper":{"arxiv_id":"2507.11574","last_updated":"2025-07-15T04:26:40Z","snapshot_observed_at":"2026-08-09T08:21:42.701137Z","submitted_at":"2025-07-15T04:26:40Z","title":"Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T17:25:43.261963Z"},"links":{"citing_paper":"/paper/2507.11574"},"observation_digest":"sha256:172b1940bf3577eea0702034c47a913e20319e26d6c83be24e7b0bf9a629a645","observation_id":"2e1335b5-acb1-48db-a461-ed843a3feaaa","resolution":{"observed_at":"2026-08-06T17:25:49.057046Z","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":"2507.06133","last_updated":"2025-07-24T19:54:06Z","snapshot_observed_at":"2026-08-09T04:34:38.123994Z","submitted_at":"2025-07-08T16:18:18Z","title":"Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions","version":2},"cited_work":{"arxiv_id":"2507.06133","doi":null,"metadata_source":"pith","pith_arxiv_id":"2507.06133","snapshot_observed_at":"2026-08-06T17:25:44.917779Z","title":"Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions","venue":"cs.CE","work_id":"94459f63-4732-4e78-acee-8d296a57823d","year":2025},"citing_paper":{"arxiv_id":"2507.11574","last_updated":"2025-07-15T04:26:40Z","snapshot_observed_at":"2026-08-09T08:21:42.701137Z","submitted_at":"2025-07-15T04:26:40Z","title":"Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T17:25:43.347399Z"},"links":{"cited_paper":"/paper/2507.06133","citing_paper":"/paper/2507.11574"},"observation_digest":"sha256:be00bd13e76754b5cf603a30233547dc1cc6e07263afcac9846c294e8453b9dd","observation_id":"92db63d2-9a95-4d6c-8c56-27ec458afee0","resolution":{"observed_at":"2026-08-06T17:25:45.068075Z","resolver_source":"local_arxiv","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:25:48.827115Z","title":"Fully convolutional network enhanced deeponet-based surrogate of predicting the travel-time fields","venue":null,"work_id":"33a256ce-7743-4a80-845e-cd2eca3cd4aa","year":2024},"citing_paper":{"arxiv_id":"2507.11574","last_updated":"2025-07-15T04:26:40Z","snapshot_observed_at":"2026-08-09T08:21:42.701137Z","submitted_at":"2025-07-15T04:26:40Z","title":"Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T17:25:43.455532Z"},"links":{"citing_paper":"/paper/2507.11574"},"observation_digest":"sha256:df5d320d4c2fdfbf719a79180cc01e1399dc9cca18061603a05504493299e1f7","observation_id":"edeca6ab-f9eb-4b53-b708-15e80a549b06","resolution":{"observed_at":"2026-08-06T17:25:48.920440Z","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":"2026-08-06T17:25:43.553381Z","title":"Porous-deeponet: Learning the solution operators of parametric reactive transport equations in porous media","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.11574","last_updated":"2025-07-15T04:26:40Z","snapshot_observed_at":"2026-08-09T08:21:42.701137Z","submitted_at":"2025-07-15T04:26:40Z","title":"Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T17:25:43.553381Z"},"links":{"citing_paper":"/paper/2507.11574"},"observation_digest":"sha256:4492244c052786d88c3858d60cafb1c6c291e9f914d63f72aa4ccac15500c6f1","observation_id":"2966884f-d824-4a89-a39d-be8bec188ffd","resolution":{"observed_at":"2026-08-06T17:25:43.553381Z","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":"2026-08-06T17:25:48.670565Z","title":"Ai-driven uncertainty quantification & multi-physics approach to evaluate cladding materials in a microreactor","venue":null,"work_id":"bfd8adee-fc6a-4c4c-a63d-bac666f45519","year":2025},"citing_paper":{"arxiv_id":"2507.11574","last_updated":"2025-07-15T04:26:40Z","snapshot_observed_at":"2026-08-09T08:21:42.701137Z","submitted_at":"2025-07-15T04:26:40Z","title":"Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T17:25:43.617573Z"},"links":{"citing_paper":"/paper/2507.11574"},"observation_digest":"sha256:ea6ee42237d889b207652d3ffc1951af418efe8ce6821a521b389ec89fb32749","observation_id":"1959583a-9656-43fc-bf84-b14ec4803494","resolution":{"observed_at":"2026-08-06T17:25:48.732802Z","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":"2505.18891","last_updated":"2025-05-24T22:32:22Z","snapshot_observed_at":"2026-08-07T14:21:38.714778Z","submitted_at":"2025-05-24T22:32:22Z","title":"Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel","version":1},"cited_work":{"arxiv_id":"2505.18891","doi":null,"metadata_source":"pith","pith_arxiv_id":"2505.18891","snapshot_observed_at":"2026-08-06T17:25:44.683546Z","title":"Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel","venue":"stat.AP","work_id":"3b8b0090-7e9e-4f42-af9d-9d218f44f794","year":2025},"citing_paper":{"arxiv_id":"2507.11574","last_updated":"2025-07-15T04:26:40Z","snapshot_observed_at":"2026-08-09T08:21:42.701137Z","submitted_at":"2025-07-15T04:26:40Z","title":"Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T17:25:43.699198Z"},"links":{"cited_paper":"/paper/2505.18891","citing_paper":"/paper/2507.11574"},"observation_digest":"sha256:5064d5b5655d9e298128532058e26ee05707ee7f1aff5d5be5810c81b92affc9","observation_id":"6900e211-1504-482f-b412-bf5d918387e6","resolution":{"observed_at":"2026-08-06T17:25:44.784920Z","resolver_source":"local_arxiv","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:25:48.528230Z","title":"Ai-driven non-intrusive uncertainty quantification of advanced nuclear fuels for digital twin-enabling technology","venue":null,"work_id":"e7bece1d-601a-4e73-bcd0-d72074a60220","year":2024},"citing_paper":{"arxiv_id":"2507.11574","last_updated":"2025-07-15T04:26:40Z","snapshot_observed_at":"2026-08-09T08:21:42.701137Z","submitted_at":"2025-07-15T04:26:40Z","title":"Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T17:25:43.773308Z"},"links":{"citing_paper":"/paper/2507.11574"},"observation_digest":"sha256:d4f0f726bb71761136c60b5477d8740d795cad1cda343ca2cc860da71661ccb7","observation_id":"42f0184a-7eb3-4ca4-b809-80fca7e14b02","resolution":{"observed_at":"2026-08-06T17:25:48.583356Z","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":"2026-08-06T17:25:48.334318Z","title":"Practical applications of gaussian process with uncertainty quantification and sensitivity analysis for digital twin for accident-tolerant fuel","venue":null,"work_id":"bd759d90-f8ee-46bb-8405-90e9b5732a54","year":2023},"citing_paper":{"arxiv_id":"2507.11574","last_updated":"2025-07-15T04:26:40Z","snapshot_observed_at":"2026-08-09T08:21:42.701137Z","submitted_at":"2025-07-15T04:26:40Z","title":"Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T17:25:43.844815Z"},"links":{"citing_paper":"/paper/2507.11574"},"observation_digest":"sha256:abb20fa0f4119edc34e52db33eddf27623bbfe99cb7af9162dfc1924e943e8f9","observation_id":"1c804663-3fd4-4c63-9b73-83277f329ffe","resolution":{"observed_at":"2026-08-06T17:25:48.422461Z","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":"2026-08-06T17:25:48.173501Z","title":"Uncertainty quantification and sensitivity analysis for digital twin enabling technology: Application for bison fuel performance code","venue":null,"work_id":"286b9b0f-497a-4cd4-a088-d790bccb86c3","year":2023},"citing_paper":{"arxiv_id":"2507.11574","last_updated":"2025-07-15T04:26:40Z","snapshot_observed_at":"2026-08-09T08:21:42.701137Z","submitted_at":"2025-07-15T04:26:40Z","title":"Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T17:25:43.938120Z"},"links":{"citing_paper":"/paper/2507.11574"},"observation_digest":"sha256:1b9c0d51a1e4a655a102f2570c2c705ac9781c40b4d14512c7cd921a966a85e7","observation_id":"53d7a999-2998-4037-a37a-ee64b2945a10","resolution":{"observed_at":"2026-08-06T17:25:48.260987Z","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":"2026-08-06T17:25:47.997435Z","title":"Quantitative risk assessment of a high power density small modular reactor (SMR) core using uncertainty and sensitivity analyses","venue":null,"work_id":"1fead8f6-9416-473b-8ddd-ee1b8203b83c","year":2021},"citing_paper":{"arxiv_id":"2507.11574","last_updated":"2025-07-15T04:26:40Z","snapshot_observed_at":"2026-08-09T08:21:42.701137Z","submitted_at":"2025-07-15T04:26:40Z","title":"Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T17:25:44.002766Z"},"links":{"citing_paper":"/paper/2507.11574"},"observation_digest":"sha256:0fe89f6bc1880a2d86108fdf85373e361f8dd18b70b4cc65ef9303782fd83cc5","observation_id":"c80bcb73-c93b-4ceb-8e3e-43d28670ba7b","resolution":{"observed_at":"2026-08-06T17:25:48.094141Z","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":"2026-08-06T17:25:47.709365Z","title":"Multi-criteria decision making under uncertainties in composite materials selection and design","venue":null,"work_id":"de55b79d-4e38-4d51-8d89-e3975cdf20fb","year":2022},"citing_paper":{"arxiv_id":"2507.11574","last_updated":"2025-07-15T04:26:40Z","snapshot_observed_at":"2026-08-09T08:21:42.701137Z","submitted_at":"2025-07-15T04:26:40Z","title":"Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T17:25:44.071444Z"},"links":{"citing_paper":"/paper/2507.11574"},"observation_digest":"sha256:483540ef27a5f961f27ee15c23ef4784e0d4e4eb7429bdc8d803a90d5d6ecd85","observation_id":"7453ff49-510a-4b63-b15c-a0f13033f24c","resolution":{"observed_at":"2026-08-06T17:25:47.854631Z","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":"2026-08-06T17:25:47.358652Z","title":"Bayesian neural networks: An introduction and survey","venue":null,"work_id":"ec3097fa-639c-494a-9031-43f8cd0e35b6","year":2018},"citing_paper":{"arxiv_id":"2507.11574","last_updated":"2025-07-15T04:26:40Z","snapshot_observed_at":"2026-08-09T08:21:42.701137Z","submitted_at":"2025-07-15T04:26:40Z","title":"Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T17:25:44.149196Z"},"links":{"citing_paper":"/paper/2507.11574"},"observation_digest":"sha256:fe36b6412600a07a9624d4f4b4b58f1114a5a530c0bc6814d3ff65f36694ccbc","observation_id":"eff413c4-2542-415a-ab8b-c51e330262b4","resolution":{"observed_at":"2026-08-06T17:25:47.515343Z","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":"2026-08-06T17:25:47.106611Z","title":"Hands- on bayesian neural networks—a tutorial for deep learning users","venue":null,"work_id":"523803dd-1897-4b42-9d58-3ad22f0c78d0","year":2022},"citing_paper":{"arxiv_id":"2507.11574","last_updated":"2025-07-15T04:26:40Z","snapshot_observed_at":"2026-08-09T08:21:42.701137Z","submitted_at":"2025-07-15T04:26:40Z","title":"Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T17:25:44.203338Z"},"links":{"citing_paper":"/paper/2507.11574"},"observation_digest":"sha256:eec2402c3478e1c5f3835a0032d26e963361489dc0d7145f4fb505e58b275025","observation_id":"8d058b0d-a991-46e2-b3f5-64e0c44595fb","resolution":{"observed_at":"2026-08-06T17:25:47.208745Z","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":"2026-08-06T17:25:44.269746Z","title":"Simple and scalable predictive uncertainty estimation using deep ensembles","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.11574","last_updated":"2025-07-15T04:26:40Z","snapshot_observed_at":"2026-08-09T08:21:42.701137Z","submitted_at":"2025-07-15T04:26:40Z","title":"Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T17:25:44.269746Z"},"links":{"citing_paper":"/paper/2507.11574"},"observation_digest":"sha256:6a2b5028f1c5efefc895cf258a98e04ee4ca1d16eb4c9d646bb88a246041910a","observation_id":"05aaea96-9994-45ee-9717-cc4372916530","resolution":{"observed_at":"2026-08-06T17:25:44.269746Z","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":"2026-08-06T17:25:46.869552Z","title":"Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift","venue":null,"work_id":"837081ff-7a94-4c42-91f5-01abf175d2b6","year":2019},"citing_paper":{"arxiv_id":"2507.11574","last_updated":"2025-07-15T04:26:40Z","snapshot_observed_at":"2026-08-09T08:21:42.701137Z","submitted_at":"2025-07-15T04:26:40Z","title":"Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T17:25:44.369286Z"},"links":{"citing_paper":"/paper/2507.11574"},"observation_digest":"sha256:60d2193c68f4c91bad84b429703f35dea23c9bccbdbe57a90454dc9366fd1fe9","observation_id":"cfcbd673-c7a3-4fd0-8fdc-fd6f99cc7cba","resolution":{"observed_at":"2026-08-06T17:25:46.959605Z","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":"2026-08-06T17:25:46.707986Z","title":"Distribution free uncertainty quantification for neuroscience-inspired deep neural operators","venue":null,"work_id":"15097d7e-5fe8-41a7-9cbe-2caddd63a8f4","year":2025},"citing_paper":{"arxiv_id":"2507.11574","last_updated":"2025-07-15T04:26:40Z","snapshot_observed_at":"2026-08-09T08:21:42.701137Z","submitted_at":"2025-07-15T04:26:40Z","title":"Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T17:25:44.430237Z"},"links":{"citing_paper":"/paper/2507.11574"},"observation_digest":"sha256:b6dd2abec248545b871eb90ea63cd53c0be06e6f12667e36ef28c3276776743c","observation_id":"ef992ed3-7abd-4aab-9e7d-6f6ee3d01682","resolution":{"observed_at":"2026-08-06T17:25:46.768218Z","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":"2026-08-06T17:25:46.549471Z","title":"Gaussian processes in machine learning","venue":null,"work_id":"1aeb82c1-36e6-4c3f-85a8-a248a606246a","year":2003},"citing_paper":{"arxiv_id":"2507.11574","last_updated":"2025-07-15T04:26:40Z","snapshot_observed_at":"2026-08-09T08:21:42.701137Z","submitted_at":"2025-07-15T04:26:40Z","title":"Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T17:25:44.451035Z"},"links":{"citing_paper":"/paper/2507.11574"},"observation_digest":"sha256:109525d54a0e930580dec51268ad1c229025d4afb27f9de4c1671cbc07d960fb","observation_id":"678c41f0-0b79-46bf-80cc-6be4a93cdfd7","resolution":{"observed_at":"2026-08-06T17:25:46.634618Z","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":"2026-08-06T17:25:46.262204Z","title":"Randomized prior functions for deep reinforcement learning","venue":null,"work_id":"3bec3c44-93a6-4f1b-a88f-9ff9224de493","year":2018},"citing_paper":{"arxiv_id":"2507.11574","last_updated":"2025-07-15T04:26:40Z","snapshot_observed_at":"2026-08-09T08:21:42.701137Z","submitted_at":"2025-07-15T04:26:40Z","title":"Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T17:25:44.464440Z"},"links":{"citing_paper":"/paper/2507.11574"},"observation_digest":"sha256:0bbc1174f08c9aad256d18e4b33b5b5456e9c966c487395dadc93384bbe61099","observation_id":"b3b4e5ed-eeb0-4478-937b-88371195e7e2","resolution":{"observed_at":"2026-08-06T17:25:46.384082Z","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":"2026-08-06T17:25:45.994330Z","title":"Analytical model for estimating terrestrial cosmic ray fluxes nearly anytime and anywhere in the world: Extension of parma/expacs","venue":null,"work_id":"4e1bebb0-21ca-44d0-89a9-cb2e213a82db","year":2015},"citing_paper":{"arxiv_id":"2507.11574","last_updated":"2025-07-15T04:26:40Z","snapshot_observed_at":"2026-08-09T08:21:42.701137Z","submitted_at":"2025-07-15T04:26:40Z","title":"Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T17:25:44.487719Z"},"links":{"citing_paper":"/paper/2507.11574"},"observation_digest":"sha256:0921a0f195ae9e1ed357ba115465574fa74dfe7dd20659a727aa7d3eab6b4c25","observation_id":"bd1b060a-6bac-4ec7-8650-78f97d30407a","resolution":{"observed_at":"2026-08-06T17:25:46.121147Z","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":"2026-08-06T17:25:45.811489Z","title":"Analytical model for estimating the zenith angle dependence of terrestrial cosmic ray fluxes","venue":null,"work_id":"32a17ee6-fa4a-4db5-9450-12bb8e643844","year":2016},"citing_paper":{"arxiv_id":"2507.11574","last_updated":"2025-07-15T04:26:40Z","snapshot_observed_at":"2026-08-09T08:21:42.701137Z","submitted_at":"2025-07-15T04:26:40Z","title":"Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T17:25:44.511551Z"},"links":{"citing_paper":"/paper/2507.11574"},"observation_digest":"sha256:245293dd81791182d7e9fc42f652af0c78c026ecdaf87d625536f8b95454f365","observation_id":"ad05f7ce-ce7c-4257-ac16-0582b316c11f","resolution":{"observed_at":"2026-08-06T17:25:45.890876Z","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":"2026-08-06T17:25:45.619277Z","title":"EXPACS: EXcel-based program for calculating atmospheric cosmic-ray spectrum","venue":null,"work_id":"9d192e7a-495a-4f87-80b8-2a5cc61e131b","year":2024},"citing_paper":{"arxiv_id":"2507.11574","last_updated":"2025-07-15T04:26:40Z","snapshot_observed_at":"2026-08-09T08:21:42.701137Z","submitted_at":"2025-07-15T04:26:40Z","title":"Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T17:25:44.543496Z"},"links":{"citing_paper":"/paper/2507.11574"},"observation_digest":"sha256:2927cc13e43f6eccb42925fb3e2b11f103ec5783131328aab5ba212bf9e9364c","observation_id":"2aa07288-0717-46ff-b607-5485cabcf59b","resolution":{"observed_at":"2026-08-06T17:25:45.709565Z","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":"2026-08-06T17:25:45.485792Z","title":"Benchmark study of particle and heavy-ion transport code system using shielding integral benchmark archive and database for accelerator-shielding experiments","venue":null,"work_id":"3a143cff-e748-42cf-a12d-6ddb7cd27128","year":2022},"citing_paper":{"arxiv_id":"2507.11574","last_updated":"2025-07-15T04:26:40Z","snapshot_observed_at":"2026-08-09T08:21:42.701137Z","submitted_at":"2025-07-15T04:26:40Z","title":"Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T17:25:44.573785Z"},"links":{"citing_paper":"/paper/2507.11574"},"observation_digest":"sha256:461f1cc3b77ceef8d957a0e6b0bb9497727a1ff4654e19cec7dcde4414eb1f8f","observation_id":"410ebdea-b202-4a23-ace2-917e4c04f3db","resolution":{"observed_at":"2026-08-06T17:25:45.532719Z","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":"2026-08-06T17:25:45.319831Z","title":"Recent improvements of the particle and heavy ion transport code system–phits version 3.33","venue":null,"work_id":"30a84562-4614-403d-9aa8-1b32186f485c","year":2024},"citing_paper":{"arxiv_id":"2507.11574","last_updated":"2025-07-15T04:26:40Z","snapshot_observed_at":"2026-08-09T08:21:42.701137Z","submitted_at":"2025-07-15T04:26:40Z","title":"Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T17:25:44.618938Z"},"links":{"citing_paper":"/paper/2507.11574"},"observation_digest":"sha256:402c7932b37d8ef618fcf46e2e4f0a168e84b66ed92441ce7e2006969792ca66","observation_id":"0f8f797a-e8ed-4767-ba03-723fdfd87aec","resolution":{"observed_at":"2026-08-06T17:25:45.407462Z","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":"2507.11574","last_updated":"2025-07-15T04:26:40Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-09T08:21:42.701137Z","submitted_at":"2025-07-15T04:26:40Z","title":"Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators"},"reference_resolution":{"displayed":38,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":12,"verified_exact":2,"verified_fuzzy":24},"total_outbound_references":38},"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 38 of 38 outbound references and 1 inbound Pith citation observation for arXiv:2507.11574."}