{"as_of":"2026-08-16T08:58:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:bdcb2383996d19b6d5ceea23f7ba62bd57a73e84fbe0494954b8d40cf36aadf3","coverage":[{"denominator":35,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":35,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T13:11:33.795444Z","state":"measured"},{"denominator":35,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":35,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+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/1908.05835/citation-record","integrity":"/paper/1908.05835/integrity","json":"/paper/1908.05835/citation-record.json","paper":"/paper/1908.05835"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T13:11:35.112610Z","title":"Estima tion of Spatially Correlated Random Fields in Heterogeneous Wireless Sensor Networks,","venue":null,"work_id":"77717540-96c6-43e0-adb8-b375d381e8e7","year":2015},"citing_paper":{"arxiv_id":"1908.05835","last_updated":"2020-07-20T09:42:26Z","snapshot_observed_at":"2026-08-16T02:25:02.980959Z","submitted_at":"2019-08-16T03:58:27Z","title":"Bayesian Spatial Field Reconstruction with Unknown Distortions in Sensor Networks","version":4},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T13:11:33.442042Z"},"links":{"citing_paper":"/paper/1908.05835"},"observation_digest":"sha256:0021315292c518940fa8c3058d88e9dd65de8837033f0a7bb92922c8325e1827","observation_id":"90a85f6b-1443-4ce2-a5ea-61ef5b055b0c","resolution":{"observed_at":"2026-08-14T13:11:35.119467Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:11:35.069031Z","title":"Optimal multi-type s ensor placements in gaussian spatial ﬁelds for environmental monitoring,","venue":null,"work_id":"5493fd3b-b4d1-49d3-be3e-1b229ad59fbb","year":2018},"citing_paper":{"arxiv_id":"1908.05835","last_updated":"2020-07-20T09:42:26Z","snapshot_observed_at":"2026-08-16T02:25:02.980959Z","submitted_at":"2019-08-16T03:58:27Z","title":"Bayesian Spatial Field Reconstruction with Unknown Distortions in Sensor Networks","version":4},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-14T13:11:33.456441Z"},"links":{"citing_paper":"/paper/1908.05835"},"observation_digest":"sha256:9f17feddb1d3ed11788575aae4c31f8f3a2b53db18e09eb645d25200b741b9b4","observation_id":"e83caa07-94f5-451d-9b96-4c9fc9b7e039","resolution":{"observed_at":"2026-08-14T13:11:35.074939Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:11:35.026759Z","title":"Short-ter m solar power forecasting based on weighted gaussian process regression,","venue":null,"work_id":"05d12a25-c0b4-45c0-bae6-21ddce37bbfd","year":2018},"citing_paper":{"arxiv_id":"1908.05835","last_updated":"2020-07-20T09:42:26Z","snapshot_observed_at":"2026-08-16T02:25:02.980959Z","submitted_at":"2019-08-16T03:58:27Z","title":"Bayesian Spatial Field Reconstruction with Unknown Distortions in Sensor Networks","version":4},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T13:11:33.464371Z"},"links":{"citing_paper":"/paper/1908.05835"},"observation_digest":"sha256:219bd854d955faf6ea60004157161f3f69301c1589267dec44d48e5faec3078f","observation_id":"ae4651b4-ba92-4e20-b4f8-99fcdf995332","resolution":{"observed_at":"2026-08-14T13:11:35.046991Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:11:34.987583Z","title":"Sohraby , D","venue":null,"work_id":"21cf007e-e34e-48bd-82b7-6689b204ed59","year":2007},"citing_paper":{"arxiv_id":"1908.05835","last_updated":"2020-07-20T09:42:26Z","snapshot_observed_at":"2026-08-16T02:25:02.980959Z","submitted_at":"2019-08-16T03:58:27Z","title":"Bayesian Spatial Field Reconstruction with Unknown Distortions in Sensor Networks","version":4},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-14T13:11:33.472805Z"},"links":{"citing_paper":"/paper/1908.05835"},"observation_digest":"sha256:90465ae9fea2736c8711119aa0de77427a4ba2d832fb7281b52e4f59f51fee7a","observation_id":"61d1422f-1463-4650-b92a-8f96f4347547","resolution":{"observed_at":"2026-08-14T13:11:35.003166Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:11:34.944522Z","title":"De centralized hypothesis testing in wireless sensor networks in the presence of misbehaving nodes,","venue":null,"work_id":"f607adcc-dd96-4ec5-a872-c58942d91b2c","year":2013},"citing_paper":{"arxiv_id":"1908.05835","last_updated":"2020-07-20T09:42:26Z","snapshot_observed_at":"2026-08-16T02:25:02.980959Z","submitted_at":"2019-08-16T03:58:27Z","title":"Bayesian Spatial Field Reconstruction with Unknown Distortions in Sensor Networks","version":4},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T13:11:33.480537Z"},"links":{"citing_paper":"/paper/1908.05835"},"observation_digest":"sha256:effd5e83a963f46500816461b531cf343c02625dec8347f7511d166976159712","observation_id":"32242c8d-04af-4678-873d-f5cd292b3626","resolution":{"observed_at":"2026-08-14T13:11:34.951071Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:11:34.879623Z","title":"Calibration of mul ti-target tracking algorithms using non-cooperative targets,","venue":null,"work_id":"7d62cce4-c17d-453e-aa46-d130edd4819a","year":2013},"citing_paper":{"arxiv_id":"1908.05835","last_updated":"2020-07-20T09:42:26Z","snapshot_observed_at":"2026-08-16T02:25:02.980959Z","submitted_at":"2019-08-16T03:58:27Z","title":"Bayesian Spatial Field Reconstruction with Unknown Distortions in Sensor Networks","version":4},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T13:11:33.488541Z"},"links":{"citing_paper":"/paper/1908.05835"},"observation_digest":"sha256:d34678ea15e372b92f4202acc608f2e2d73afd47ef84342f4dffe2e7f18a8ada","observation_id":"3bfc4b45-51da-4d1a-8785-6fe5fa286727","resolution":{"observed_at":"2026-08-14T13:11:34.900836Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:11:34.851470Z","title":"Draft roadmap for next generation air monit oring,","venue":null,"work_id":"a2207540-b3f8-40f1-8299-90677b18154e","year":2013},"citing_paper":{"arxiv_id":"1908.05835","last_updated":"2020-07-20T09:42:26Z","snapshot_observed_at":"2026-08-16T02:25:02.980959Z","submitted_at":"2019-08-16T03:58:27Z","title":"Bayesian Spatial Field Reconstruction with Unknown Distortions in Sensor Networks","version":4},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T13:11:33.499476Z"},"links":{"citing_paper":"/paper/1908.05835"},"observation_digest":"sha256:17144f6979717a9e6693893d602884378f4434cc5575e15e2327b3d8ee3d8d4d","observation_id":"16b49fcb-1141-43e9-8023-6e8cb9b7f02e","resolution":{"observed_at":"2026-08-14T13:11:34.860891Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:11:34.822831Z","title":"Model-based ren dezvous calibration of mobile sensor networks for monitoring air quality ,","venue":null,"work_id":"4a82c790-cf0f-44ac-9230-f6dfcf752990","year":2015},"citing_paper":{"arxiv_id":"1908.05835","last_updated":"2020-07-20T09:42:26Z","snapshot_observed_at":"2026-08-16T02:25:02.980959Z","submitted_at":"2019-08-16T03:58:27Z","title":"Bayesian Spatial Field Reconstruction with Unknown Distortions in Sensor Networks","version":4},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T13:11:33.508983Z"},"links":{"citing_paper":"/paper/1908.05835"},"observation_digest":"sha256:1a11db7263b6ccf53c99b62ec7970e4b8355de94156844011b2d176328357cde","observation_id":"0559f0b7-ebba-40d6-bd83-ed3fba8de110","resolution":{"observed_at":"2026-08-14T13:11:34.833534Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:11:34.783424Z","title":"Random access sen sor networks: Field reconstruction from incomplete data,","venue":null,"work_id":"41f3d080-635d-41b2-9f60-c05ea49212e4","year":2012},"citing_paper":{"arxiv_id":"1908.05835","last_updated":"2020-07-20T09:42:26Z","snapshot_observed_at":"2026-08-16T02:25:02.980959Z","submitted_at":"2019-08-16T03:58:27Z","title":"Bayesian Spatial Field Reconstruction with Unknown Distortions in Sensor Networks","version":4},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T13:11:33.515990Z"},"links":{"citing_paper":"/paper/1908.05835"},"observation_digest":"sha256:8d363021f4ba22802f8b1a9fb6557b603c9d668ae49410cd6e93029513c1aff6","observation_id":"96f3b51d-7f8f-4366-aefd-302bc21e6c06","resolution":{"observed_at":"2026-08-14T13:11:34.795961Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:11:34.747042Z","title":"Quality of information in mobile crowdsensing: Survey and research challenges,","venue":null,"work_id":"b689ea7e-075e-42c0-9390-f07437af513b","year":2017},"citing_paper":{"arxiv_id":"1908.05835","last_updated":"2020-07-20T09:42:26Z","snapshot_observed_at":"2026-08-16T02:25:02.980959Z","submitted_at":"2019-08-16T03:58:27Z","title":"Bayesian Spatial Field Reconstruction with Unknown Distortions in Sensor Networks","version":4},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T13:11:33.521653Z"},"links":{"citing_paper":"/paper/1908.05835"},"observation_digest":"sha256:44bb3fcb9bb724b2609e5396d8ef91660a91f03ccb959f00f9d3edd12b3829c0","observation_id":"f06d260a-1ae1-4b08-aa7e-e8a01892a67a","resolution":{"observed_at":"2026-08-14T13:11:34.758272Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:11:34.723392Z","title":"Using multi-parameters for calibr ation of low-cost sensors in urban environment,","venue":null,"work_id":"189b2de3-a6c8-4a43-ac88-4ba22c84beaf","year":2017},"citing_paper":{"arxiv_id":"1908.05835","last_updated":"2020-07-20T09:42:26Z","snapshot_observed_at":"2026-08-16T02:25:02.980959Z","submitted_at":"2019-08-16T03:58:27Z","title":"Bayesian Spatial Field Reconstruction with Unknown Distortions in Sensor Networks","version":4},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T13:11:33.541741Z"},"links":{"citing_paper":"/paper/1908.05835"},"observation_digest":"sha256:e0759f0037a1fef412a39f89d1568a9a5db3d6a71ac77bcdcf5aa28e91977aef","observation_id":"0a1b27bd-7ce4-4a56-8b34-d3ea75732f61","resolution":{"observed_at":"2026-08-14T13:11:34.729068Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:11:34.696632Z","title":"Possible artifacts of data biases in the recen t global surface warming hiatus,","venue":null,"work_id":"5e393922-1e64-4e62-a739-4f7ee0be43b1","year":2015},"citing_paper":{"arxiv_id":"1908.05835","last_updated":"2020-07-20T09:42:26Z","snapshot_observed_at":"2026-08-16T02:25:02.980959Z","submitted_at":"2019-08-16T03:58:27Z","title":"Bayesian Spatial Field Reconstruction with Unknown Distortions in Sensor Networks","version":4},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T13:11:33.553187Z"},"links":{"citing_paper":"/paper/1908.05835"},"observation_digest":"sha256:9a0fd34e661ec219e5c3104132c7d47ba931180e4351095e034fde20abd519e9","observation_id":"e0c1456d-15b7-4446-8c9d-fa72f776ca0a","resolution":{"observed_at":"2026-08-14T13:11:34.704814Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:11:34.672310Z","title":"Convex o ptimization approaches for blind sensor calibration using sparsity ,","venue":null,"work_id":"aa6ce5e6-6f6e-4cd0-8bcf-1d62b71ddb26","year":2014},"citing_paper":{"arxiv_id":"1908.05835","last_updated":"2020-07-20T09:42:26Z","snapshot_observed_at":"2026-08-16T02:25:02.980959Z","submitted_at":"2019-08-16T03:58:27Z","title":"Bayesian Spatial Field Reconstruction with Unknown Distortions in Sensor Networks","version":4},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T13:11:33.572595Z"},"links":{"citing_paper":"/paper/1908.05835"},"observation_digest":"sha256:b52d03ce57a26befd9e5d3c188ffc9f0f43bd772f00c07000b139a7caca815d7","observation_id":"7fccc67b-a261-4da7-aaf2-152a25de2712","resolution":{"observed_at":"2026-08-14T13:11:34.677622Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:11:34.598270Z","title":"On re ndezvous in mobile sensing networks,","venue":null,"work_id":"7d903946-5a81-4f24-a5cf-3bfeec25e738","year":2014},"citing_paper":{"arxiv_id":"1908.05835","last_updated":"2020-07-20T09:42:26Z","snapshot_observed_at":"2026-08-16T02:25:02.980959Z","submitted_at":"2019-08-16T03:58:27Z","title":"Bayesian Spatial Field Reconstruction with Unknown Distortions in Sensor Networks","version":4},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-14T13:11:33.584793Z"},"links":{"citing_paper":"/paper/1908.05835"},"observation_digest":"sha256:b20aafdbd67b36b96fbcd99300950860eeb7447b54d8bbed8c2d9a485dfa1e2a","observation_id":"7e1d7ef5-a329-42b7-b820-401c1e4a009a","resolution":{"observed_at":"2026-08-14T13:11:34.615910Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:11:34.531870Z","title":"Info rmed nonnegative matrix factorization methods for mobile sensor network calibration,","venue":null,"work_id":"78f6fed8-51b8-4777-8721-29800322a036","year":2018},"citing_paper":{"arxiv_id":"1908.05835","last_updated":"2020-07-20T09:42:26Z","snapshot_observed_at":"2026-08-16T02:25:02.980959Z","submitted_at":"2019-08-16T03:58:27Z","title":"Bayesian Spatial Field Reconstruction with Unknown Distortions in Sensor Networks","version":4},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-14T13:11:33.590758Z"},"links":{"citing_paper":"/paper/1908.05835"},"observation_digest":"sha256:59c46c4f8c6bbadaafb8d9db2f729e63da89f04d4144e00f754862256e138514","observation_id":"f36efda4-b7a2-4ca9-bbc5-79e9eb278dff","resolution":{"observed_at":"2026-08-14T13:11:34.553322Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:11:34.484565Z","title":"Sensor calibration fo r off-the-grid spectral estimation,","venue":null,"work_id":"294dc3c1-c372-4e40-a638-a16371e8e25a","year":2018},"citing_paper":{"arxiv_id":"1908.05835","last_updated":"2020-07-20T09:42:26Z","snapshot_observed_at":"2026-08-16T02:25:02.980959Z","submitted_at":"2019-08-16T03:58:27Z","title":"Bayesian Spatial Field Reconstruction with Unknown Distortions in Sensor Networks","version":4},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-14T13:11:33.599469Z"},"links":{"citing_paper":"/paper/1908.05835"},"observation_digest":"sha256:2e8d202d0b1ad8dbe95d771732372ca1dd249212ae1b08f187426b049fc3a12c","observation_id":"08b62531-b836-413e-b55a-99f886806963","resolution":{"observed_at":"2026-08-14T13:11:34.494995Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:11:34.460059Z","title":"A non-convex approach to joi nt sensor calibration and spectrum estimation,","venue":null,"work_id":"92272b25-0079-4113-b156-00c3367c214b","year":2018},"citing_paper":{"arxiv_id":"1908.05835","last_updated":"2020-07-20T09:42:26Z","snapshot_observed_at":"2026-08-16T02:25:02.980959Z","submitted_at":"2019-08-16T03:58:27Z","title":"Bayesian Spatial Field Reconstruction with Unknown Distortions in Sensor Networks","version":4},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-14T13:11:33.607327Z"},"links":{"citing_paper":"/paper/1908.05835"},"observation_digest":"sha256:fc0f35f167bf894ee224d0e970f25cd88c83f3be3362e38cab0d472ab7958bac","observation_id":"30f35d07-b245-459f-9ec2-7e41294f985a","resolution":{"observed_at":"2026-08-14T13:11:34.472063Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:11:34.422739Z","title":"A secure opti mum distributed detection scheme in under-attack wireless sensor networks,","venue":null,"work_id":"5f11f2b2-8449-4536-849e-a13412a5fab2","year":2018},"citing_paper":{"arxiv_id":"1908.05835","last_updated":"2020-07-20T09:42:26Z","snapshot_observed_at":"2026-08-16T02:25:02.980959Z","submitted_at":"2019-08-16T03:58:27Z","title":"Bayesian Spatial Field Reconstruction with Unknown Distortions in Sensor Networks","version":4},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-14T13:11:33.617505Z"},"links":{"citing_paper":"/paper/1908.05835"},"observation_digest":"sha256:8081d42ab48defa6f3389a2b4d49d809f916f320c6040395a06a826f0302f493","observation_id":"b491d421-d2ed-4310-9bac-91cdb593a96e","resolution":{"observed_at":"2026-08-14T13:11:34.431953Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:11:34.394099Z","title":"Collaborative spectrum sensing in the presence of byzantine attacks in cognitive radio networks,","venue":null,"work_id":"5b8096b4-e178-482a-82a5-528e22941383","year":2010},"citing_paper":{"arxiv_id":"1908.05835","last_updated":"2020-07-20T09:42:26Z","snapshot_observed_at":"2026-08-16T02:25:02.980959Z","submitted_at":"2019-08-16T03:58:27Z","title":"Bayesian Spatial Field Reconstruction with Unknown Distortions in Sensor Networks","version":4},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-14T13:11:33.630468Z"},"links":{"citing_paper":"/paper/1908.05835"},"observation_digest":"sha256:04a3b3dd95949d7479ef3731480308a952e5d363fc1d339c503be025d1c6ee36","observation_id":"1aac07da-1607-4823-80ed-1a33332bd6d1","resolution":{"observed_at":"2026-08-14T13:11:34.403547Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:11:34.362416Z","title":"Distributed ev ent detection under byzantine attack in wireless sensor 40 networks,","venue":null,"work_id":"826bcb4d-c49b-45ca-a604-064c20fd3fc4","year":2014},"citing_paper":{"arxiv_id":"1908.05835","last_updated":"2020-07-20T09:42:26Z","snapshot_observed_at":"2026-08-16T02:25:02.980959Z","submitted_at":"2019-08-16T03:58:27Z","title":"Bayesian Spatial Field Reconstruction with Unknown Distortions in Sensor Networks","version":4},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-14T13:11:33.640140Z"},"links":{"citing_paper":"/paper/1908.05835"},"observation_digest":"sha256:61b3dfcc9302fd8c45dfc0656a411f30927d5071a5e9d86c74c2d37094eda508","observation_id":"889ccf02-7295-47bc-871c-09495bbc3888","resolution":{"observed_at":"2026-08-14T13:11:34.374131Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:11:34.326413Z","title":"Crowdsourcing urban survei llance: The development of homeland security markets for environmental sensor networks,","venue":null,"work_id":"ae0db3ab-da63-4488-b165-caf5d8aa1ad8","year":2013},"citing_paper":{"arxiv_id":"1908.05835","last_updated":"2020-07-20T09:42:26Z","snapshot_observed_at":"2026-08-16T02:25:02.980959Z","submitted_at":"2019-08-16T03:58:27Z","title":"Bayesian Spatial Field Reconstruction with Unknown Distortions in Sensor Networks","version":4},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-14T13:11:33.649032Z"},"links":{"citing_paper":"/paper/1908.05835"},"observation_digest":"sha256:871dd574acc062cebdb36e90d7ec57ac86f8d0082c74b5d5a4c0f0de080f6519","observation_id":"f9672c1e-9897-4d53-9ec8-8489925afa21","resolution":{"observed_at":"2026-08-14T13:11:34.343226Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:11:34.294072Z","title":"Crowd-based learning of spatial ﬁelds for the internet of things: From harvesting of data to inference,","venue":null,"work_id":"5a3b25d7-fb1f-4f20-bf94-03ea8eac04bc","year":2018},"citing_paper":{"arxiv_id":"1908.05835","last_updated":"2020-07-20T09:42:26Z","snapshot_observed_at":"2026-08-16T02:25:02.980959Z","submitted_at":"2019-08-16T03:58:27Z","title":"Bayesian Spatial Field Reconstruction with Unknown Distortions in Sensor Networks","version":4},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-14T13:11:33.660308Z"},"links":{"citing_paper":"/paper/1908.05835"},"observation_digest":"sha256:83833d382c48273f79c2f338fac14d8636e8bfd7b6dce2eda59d7f5ba214e4df","observation_id":"4277cc43-1e6e-4f26-abdf-fad58268b0e3","resolution":{"observed_at":"2026-08-14T13:11:34.306049Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:11:34.256664Z","title":"Spatial ﬁeld reconstruction and sensor selection in heterogeneous sensor networks with stochastic energy ha rvesting,","venue":null,"work_id":"13fd5b2e-3270-4c76-bc97-c8548698eade","year":2018},"citing_paper":{"arxiv_id":"1908.05835","last_updated":"2020-07-20T09:42:26Z","snapshot_observed_at":"2026-08-16T02:25:02.980959Z","submitted_at":"2019-08-16T03:58:27Z","title":"Bayesian Spatial Field Reconstruction with Unknown Distortions in Sensor Networks","version":4},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-14T13:11:33.672829Z"},"links":{"citing_paper":"/paper/1908.05835"},"observation_digest":"sha256:fb16456cabc2fcb8a9ca0f67d23cd8e8aae7a893175ba849645398910e67ccb7","observation_id":"3d33b368-94ef-4f7c-831b-971e7daa5325","resolution":{"observed_at":"2026-08-14T13:11:34.266564Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:11:34.224380Z","title":"How to Utilize Sen sor Network Data to Efﬁciently Perform Model Calibration and Spatial Field Reconstruction,","venue":null,"work_id":"50cf022d-02cc-4098-9167-d4575bd4d64f","year":2015},"citing_paper":{"arxiv_id":"1908.05835","last_updated":"2020-07-20T09:42:26Z","snapshot_observed_at":"2026-08-16T02:25:02.980959Z","submitted_at":"2019-08-16T03:58:27Z","title":"Bayesian Spatial Field Reconstruction with Unknown Distortions in Sensor Networks","version":4},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-14T13:11:33.680932Z"},"links":{"citing_paper":"/paper/1908.05835"},"observation_digest":"sha256:8567b2442e0be9313a110030ac791ab5908b44937abf8da7d41ecebf82c4f34c","observation_id":"eb793c1c-9b89-404e-b00d-cd333a81e706","resolution":{"observed_at":"2026-08-14T13:11:34.235576Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:11:34.189896Z","title":"Random Field Reconstruction With Quantization in Wireless Sensor Networks,","venue":null,"work_id":"4a4670bf-0f3b-4ae2-932e-0c0fde45920f","year":2013},"citing_paper":{"arxiv_id":"1908.05835","last_updated":"2020-07-20T09:42:26Z","snapshot_observed_at":"2026-08-16T02:25:02.980959Z","submitted_at":"2019-08-16T03:58:27Z","title":"Bayesian Spatial Field Reconstruction with Unknown Distortions in Sensor Networks","version":4},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-14T13:11:33.692875Z"},"links":{"citing_paper":"/paper/1908.05835"},"observation_digest":"sha256:9c8cdc875c0cbcb2bfde7253890b3f57fbc64b0b29baa115ff3b8c78f0452d2b","observation_id":"8580242b-3ed1-4838-a58d-34fd34958b12","resolution":{"observed_at":"2026-08-14T13:11:34.202472Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:11:34.168785Z","title":"Sampling and reconst ruction of spatial ﬁelds using mobile sensors,","venue":null,"work_id":"b28c4d4d-8f47-4de2-9df3-27cd4692c065","year":2013},"citing_paper":{"arxiv_id":"1908.05835","last_updated":"2020-07-20T09:42:26Z","snapshot_observed_at":"2026-08-16T02:25:02.980959Z","submitted_at":"2019-08-16T03:58:27Z","title":"Bayesian Spatial Field Reconstruction with Unknown Distortions in Sensor Networks","version":4},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-14T13:11:33.706669Z"},"links":{"citing_paper":"/paper/1908.05835"},"observation_digest":"sha256:740f6f9bbde80dcb053137264c9c57dacc11b6003364d431051126f00e42c38f","observation_id":"6b0b6b1d-c5e4-4630-bb7f-3798cec79c9b","resolution":{"observed_at":"2026-08-14T13:11:34.174157Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:11:34.140474Z","title":"Estimating spatial averages of envi ronmental parameters based on mobile crowdsensing,","venue":null,"work_id":"a55b37b1-8f7b-48c1-83e7-f2c5409d28b7","year":2017},"citing_paper":{"arxiv_id":"1908.05835","last_updated":"2020-07-20T09:42:26Z","snapshot_observed_at":"2026-08-16T02:25:02.980959Z","submitted_at":"2019-08-16T03:58:27Z","title":"Bayesian Spatial Field Reconstruction with Unknown Distortions in Sensor Networks","version":4},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-14T13:11:33.715859Z"},"links":{"citing_paper":"/paper/1908.05835"},"observation_digest":"sha256:19ef75cf020346c0562b90ca631ef53dd2ddcf29b4f3728af769c67b80ab86aa","observation_id":"71d98bdc-043e-489f-ae1f-a57fc13bdad8","resolution":{"observed_at":"2026-08-14T13:11:34.148339Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:11:34.102140Z","title":"A trust-base d mixture of gaussian processes model for reliable regression in participatory sensing","venue":null,"work_id":"7ab34842-6232-43b9-95ea-76b7951453f7","year":2017},"citing_paper":{"arxiv_id":"1908.05835","last_updated":"2020-07-20T09:42:26Z","snapshot_observed_at":"2026-08-16T02:25:02.980959Z","submitted_at":"2019-08-16T03:58:27Z","title":"Bayesian Spatial Field Reconstruction with Unknown Distortions in Sensor Networks","version":4},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-14T13:11:33.723959Z"},"links":{"citing_paper":"/paper/1908.05835"},"observation_digest":"sha256:146c94d12f9137bf80e29cf899cafae2e0f38d2f1b412cdae19dd9e298c4ab99","observation_id":"eba3b125-bf27-4871-b440-f865e17985fd","resolution":{"observed_at":"2026-08-14T13:11:34.112315Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:11:34.074320Z","title":"Location-aw are cooperative spectrum sensing via Gaussian Processes,","venue":null,"work_id":"ee6a9652-fd28-4a3e-94de-9c6688a86a39","year":2012},"citing_paper":{"arxiv_id":"1908.05835","last_updated":"2020-07-20T09:42:26Z","snapshot_observed_at":"2026-08-16T02:25:02.980959Z","submitted_at":"2019-08-16T03:58:27Z","title":"Bayesian Spatial Field Reconstruction with Unknown Distortions in Sensor Networks","version":4},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-14T13:11:33.735499Z"},"links":{"citing_paper":"/paper/1908.05835"},"observation_digest":"sha256:285864b11b0fc142ca2d3293df6637b51994a9cbb735f2e6d6e8b592f8222260","observation_id":"1d502920-0566-4651-9983-6aa8e84dff57","resolution":{"observed_at":"2026-08-14T13:11:34.084439Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:11:34.030352Z","title":null,"venue":null,"work_id":"05936156-07a4-4924-ae14-695b45e765a0","year":2005},"citing_paper":{"arxiv_id":"1908.05835","last_updated":"2020-07-20T09:42:26Z","snapshot_observed_at":"2026-08-16T02:25:02.980959Z","submitted_at":"2019-08-16T03:58:27Z","title":"Bayesian Spatial Field Reconstruction with Unknown Distortions in Sensor Networks","version":4},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-14T13:11:33.742701Z"},"links":{"citing_paper":"/paper/1908.05835"},"observation_digest":"sha256:766078d54878877b45af9e644d6bbd0b521dcfd42c6984095e571bc5c2405769","observation_id":"b98075bc-ffcd-4402-ae8c-bbd7b0d3c31e","resolution":{"observed_at":"2026-08-14T13:11:34.051390Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:11:33.983755Z","title":"Calibree: A self-calibration system for mobile sensor networks,","venue":null,"work_id":"77a3aeaf-aca4-4838-9aef-b91c08176e3d","year":2008},"citing_paper":{"arxiv_id":"1908.05835","last_updated":"2020-07-20T09:42:26Z","snapshot_observed_at":"2026-08-16T02:25:02.980959Z","submitted_at":"2019-08-16T03:58:27Z","title":"Bayesian Spatial Field Reconstruction with Unknown Distortions in Sensor Networks","version":4},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-14T13:11:33.753472Z"},"links":{"citing_paper":"/paper/1908.05835"},"observation_digest":"sha256:ae7c999bc8939d9dd667160a4a52204c8b1ddd4d9b430670b8ccb6e83e02dd2b","observation_id":"539b5eff-6637-4edf-9288-498dc200c459","resolution":{"observed_at":"2026-08-14T13:11:33.998400Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:11:33.954848Z","title":"The cross-entropy method for combinat orial and continuous optimization,","venue":null,"work_id":"5be8414c-1e21-49c1-955e-69345359cac9","year":1999},"citing_paper":{"arxiv_id":"1908.05835","last_updated":"2020-07-20T09:42:26Z","snapshot_observed_at":"2026-08-16T02:25:02.980959Z","submitted_at":"2019-08-16T03:58:27Z","title":"Bayesian Spatial Field Reconstruction with Unknown Distortions in Sensor Networks","version":4},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-14T13:11:33.764320Z"},"links":{"citing_paper":"/paper/1908.05835"},"observation_digest":"sha256:49ca0c03ee84656465f42e04ecfcdb93962d51f60b7de6ab64f640b68fd88208","observation_id":"2a0f2fde-e3e7-40be-b7c1-b252203794b0","resolution":{"observed_at":"2026-08-14T13:11:33.961586Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:11:33.927030Z","title":"A gentle tutorial of the em algorithm and its application t o parameter estimation for gaussian mixture and hidden markov models,","venue":null,"work_id":"cd2e3a8d-1677-4a3b-ba3d-6993f62d32b4","year":1998},"citing_paper":{"arxiv_id":"1908.05835","last_updated":"2020-07-20T09:42:26Z","snapshot_observed_at":"2026-08-16T02:25:02.980959Z","submitted_at":"2019-08-16T03:58:27Z","title":"Bayesian Spatial Field Reconstruction with Unknown Distortions in Sensor Networks","version":4},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-14T13:11:33.777999Z"},"links":{"citing_paper":"/paper/1908.05835"},"observation_digest":"sha256:786414cf76c12381366000ad5a4e5e2b72bafc3048f5b2042445926f761567be","observation_id":"211b4536-cc4a-4cc2-8540-2a8598c589dc","resolution":{"observed_at":"2026-08-14T13:11:33.934826Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:11:33.891070Z","title":"On the statistical analysis of dirty picture s,","venue":null,"work_id":"0bcb44fd-10e9-4644-85d0-5741821152f5","year":1986},"citing_paper":{"arxiv_id":"1908.05835","last_updated":"2020-07-20T09:42:26Z","snapshot_observed_at":"2026-08-16T02:25:02.980959Z","submitted_at":"2019-08-16T03:58:27Z","title":"Bayesian Spatial Field Reconstruction with Unknown Distortions in Sensor Networks","version":4},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-14T13:11:33.784564Z"},"links":{"citing_paper":"/paper/1908.05835"},"observation_digest":"sha256:89466ed4054af7dc6530ce9092611165f5788267e0b85893347d6202b0ee99ae","observation_id":"7a59956f-bdaf-435b-ad5d-6cc3b3a9e31e","resolution":{"observed_at":"2026-08-14T13:11:33.898794Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:11:33.856062Z","title":"Friedman, T","venue":null,"work_id":"10842343-2bc7-4bf4-a02d-1fed59b71534","year":2001},"citing_paper":{"arxiv_id":"1908.05835","last_updated":"2020-07-20T09:42:26Z","snapshot_observed_at":"2026-08-16T02:25:02.980959Z","submitted_at":"2019-08-16T03:58:27Z","title":"Bayesian Spatial Field Reconstruction with Unknown Distortions in Sensor Networks","version":4},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-14T13:11:33.795444Z"},"links":{"citing_paper":"/paper/1908.05835"},"observation_digest":"sha256:a43418e78988f6ef84db0ea3a9c7e1cb415cd0ee16344a20298a9954f9b7b3f0","observation_id":"ffe39c9e-9739-4f39-8f08-6c5028352c37","resolution":{"observed_at":"2026-08-14T13:11:33.866416Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"1908.05835","last_updated":"2020-07-20T09:42:26Z","latest_version":4,"primary_category":"eess.SP","snapshot_observed_at":"2026-08-16T02:25:02.980959Z","submitted_at":"2019-08-16T03:58:27Z","title":"Bayesian Spatial Field Reconstruction with Unknown Distortions in Sensor Networks"},"reference_resolution":{"displayed":35,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":1,"verified_exact":0,"verified_fuzzy":34},"total_outbound_references":35},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:1908.05835."}