{"as_of":"2026-08-11T03:35:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9414384c8b531cdd2473af467b2b06d8b6a1d4c0e8c5fc21921b1afab2c033dc","coverage":[{"denominator":20,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":20,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T20:51:57.398609Z","state":"measured"},{"denominator":21,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":21,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+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":"2026-05-15T09:42:05.906583Z","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":"2026-05-15T09:45:23.356465Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2507.01564","last_updated":"2025-07-12T17:04:09Z","snapshot_observed_at":"2026-08-08T02:33:52.785761Z","submitted_at":"2025-07-02T10:27:59Z","title":"Multi Source COVID-19 Detection via Kernel-Density-based Slice Sampling","version":2},"cited_work":{"arxiv_id":"2507.01564","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2507.01564","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Multi-source covid-19 detec- tion via kernel-density-based slice sampling","venue":null,"work_id":"1ca2e933-a23b-484c-a84f-d9859f933b6d","year":2025},"citing_paper":{"arxiv_id":"2603.15941","last_updated":"2026-04-27T13:52:32Z","snapshot_observed_at":"2026-07-06T22:49:19.323519Z","submitted_at":"2026-03-16T21:47:10Z","title":"Towards Fair and Robust Volumetric CT Classification via KL-Regularised Group Distributionally Robust Optimisation","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-15T09:42:05.906583Z"},"links":{"cited_paper":"/paper/2507.01564","citing_paper":"/paper/2603.15941"},"observation_digest":"sha256:18b1d524fb4b453e476025960baa5e5030969e0fae53e7d062094c0ec86e881d","observation_id":"e9bc6cb8-9c0c-48e7-a1ea-63ea822da90d","resolution":{"observed_at":"2026-05-15T09:45:23.359527Z","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/2507.01564/citation-record","integrity":"/paper/2507.01564/integrity","json":"/paper/2507.01564/citation-record.json","paper":"/paper/2507.01564"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:51:58.585749Z","title":"A large imaging database and novel deep neural ar- chitecture for covid-19 diagnosis","venue":null,"work_id":"fdb4c2fc-757d-4fc1-b40f-9951b10af4fd","year":2022},"citing_paper":{"arxiv_id":"2507.01564","last_updated":"2025-07-12T17:04:09Z","snapshot_observed_at":"2026-08-08T02:33:52.785761Z","submitted_at":"2025-07-02T10:27:59Z","title":"Multi Source COVID-19 Detection via Kernel-Density-based Slice Sampling","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T20:51:55.572225Z"},"links":{"citing_paper":"/paper/2507.01564"},"observation_digest":"sha256:784569ba34d42e227df5161c274b79d65352bff0483c33a44ca82148f3857306","observation_id":"df583acb-9f36-4836-9319-3fade1513f25","resolution":{"observed_at":"2026-08-06T20:51:58.589883Z","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":"2026-08-06T20:51:58.572572Z","title":"Data-driven covid-19 detection through medical imaging","venue":null,"work_id":"425c3bdb-2938-4e27-88af-a9b65dce59ef","year":2023},"citing_paper":{"arxiv_id":"2507.01564","last_updated":"2025-07-12T17:04:09Z","snapshot_observed_at":"2026-08-08T02:33:52.785761Z","submitted_at":"2025-07-02T10:27:59Z","title":"Multi Source COVID-19 Detection via Kernel-Density-based Slice Sampling","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T20:51:55.699244Z"},"links":{"citing_paper":"/paper/2507.01564"},"observation_digest":"sha256:d43d0634d78826da15643194d211ec788469249fc5799e1a2da8fc29bc612d6c","observation_id":"1d4e418c-d103-4dff-be4b-eb5c050ef303","resolution":{"observed_at":"2026-08-06T20:51:58.576607Z","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":"2026-08-06T20:51:55.840343Z","title":"Imagenet: A large-scale hierarchical image database","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2507.01564","last_updated":"2025-07-12T17:04:09Z","snapshot_observed_at":"2026-08-08T02:33:52.785761Z","submitted_at":"2025-07-02T10:27:59Z","title":"Multi Source COVID-19 Detection via Kernel-Density-based Slice Sampling","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T20:51:55.840343Z"},"links":{"citing_paper":"/paper/2507.01564"},"observation_digest":"sha256:f073c6966c54dc689f7c56f0a7fcd2e517a406c70b833656698387cab39adb12","observation_id":"ade8b440-7cd4-4098-afcc-26feaf5e8eaa","resolution":{"observed_at":"2026-08-06T20:51:55.840343Z","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":"2026-08-06T20:51:58.549343Z","title":"Covid- 19 computer-aided diagnosis through ai-assisted ct imaging analysis: Deploying a medical ai system","venue":null,"work_id":"c94c5a69-bc5a-4fb3-921c-5928097bc070","year":2024},"citing_paper":{"arxiv_id":"2507.01564","last_updated":"2025-07-12T17:04:09Z","snapshot_observed_at":"2026-08-08T02:33:52.785761Z","submitted_at":"2025-07-02T10:27:59Z","title":"Multi Source COVID-19 Detection via Kernel-Density-based Slice Sampling","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T20:51:55.946527Z"},"links":{"citing_paper":"/paper/2507.01564"},"observation_digest":"sha256:d08a78b59e54a3dc9ef2ba5e72154675ef55f2597d33d62d9eb140d6bc9dbb54","observation_id":"857d1672-5a34-4695-a959-c277c05dcf33","resolution":{"observed_at":"2026-08-06T20:51:58.553845Z","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":"2026-08-06T20:51:58.535072Z","title":"Strong baseline and bag of tricks for covid-19 detection of ct scans, 2023","venue":null,"work_id":"7fc212d2-b7f6-4386-a526-1662b13a9276","year":2023},"citing_paper":{"arxiv_id":"2507.01564","last_updated":"2025-07-12T17:04:09Z","snapshot_observed_at":"2026-08-08T02:33:52.785761Z","submitted_at":"2025-07-02T10:27:59Z","title":"Multi Source COVID-19 Detection via Kernel-Density-based Slice Sampling","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T20:51:56.062764Z"},"links":{"citing_paper":"/paper/2507.01564"},"observation_digest":"sha256:033c47fa7d1b8f31050788b05f90b18ad01b3d62f0949a5a6b87d1395b90ac5a","observation_id":"52b35d5e-a40c-4723-bda4-57108d228f0f","resolution":{"observed_at":"2026-08-06T20:51:58.539229Z","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":"2026-08-06T20:51:58.521690Z","title":"Bag of tricks of hybrid network for covid-19 detection of ct scans","venue":null,"work_id":"e3fe2ee1-7230-4d27-8797-a99a1c089446","year":2023},"citing_paper":{"arxiv_id":"2507.01564","last_updated":"2025-07-12T17:04:09Z","snapshot_observed_at":"2026-08-08T02:33:52.785761Z","submitted_at":"2025-07-02T10:27:59Z","title":"Multi Source COVID-19 Detection via Kernel-Density-based Slice Sampling","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T20:51:56.221479Z"},"links":{"citing_paper":"/paper/2507.01564"},"observation_digest":"sha256:7e6f356c3f087063ba65fd1b01d368453b6008dcc8fbd251537c1abe50c3a968","observation_id":"ba22ef49-2f0e-4595-9ddb-63c3749db927","resolution":{"observed_at":"2026-08-06T20:51:58.526081Z","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":"2026-08-06T20:51:58.508070Z","title":"A closer look at spatial-slice features learning for covid-19 detection","venue":null,"work_id":"6332932d-d698-465c-97f2-0b2f9d3472dd","year":2024},"citing_paper":{"arxiv_id":"2507.01564","last_updated":"2025-07-12T17:04:09Z","snapshot_observed_at":"2026-08-08T02:33:52.785761Z","submitted_at":"2025-07-02T10:27:59Z","title":"Multi Source COVID-19 Detection via Kernel-Density-based Slice Sampling","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T20:51:56.373572Z"},"links":{"citing_paper":"/paper/2507.01564"},"observation_digest":"sha256:77350128f65b2c671ba562dce68ee01edc7178f25c77046e4074bb25b7d114b7","observation_id":"05fdff1b-3ad1-40d0-81d6-8c40ea3511a3","resolution":{"observed_at":"2026-08-06T20:51:58.512524Z","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":"2026-08-06T20:51:58.334031Z","title":"Simple 2d convolutional neural network-based ap- proach for covid-19 detection, 2024","venue":null,"work_id":"e6cabb3a-027e-4709-bc52-b3ce31bf3bdf","year":2024},"citing_paper":{"arxiv_id":"2507.01564","last_updated":"2025-07-12T17:04:09Z","snapshot_observed_at":"2026-08-08T02:33:52.785761Z","submitted_at":"2025-07-02T10:27:59Z","title":"Multi Source COVID-19 Detection via Kernel-Density-based Slice Sampling","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T20:51:56.434535Z"},"links":{"citing_paper":"/paper/2507.01564"},"observation_digest":"sha256:deea65bb7a372c7ebbc2aa217fe6b827fcc60ac2b324049416b5314c459a4f38","observation_id":"eb6d139f-aed0-451b-a377-1b68d7724d36","resolution":{"observed_at":"2026-08-06T20:51:58.407929Z","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":"2026-08-06T20:51:58.201164Z","title":"Deep neural archi- tectures for prediction in healthcare","venue":null,"work_id":"c4c2535e-f45e-4795-b051-4fd87a79e7fa","year":2018},"citing_paper":{"arxiv_id":"2507.01564","last_updated":"2025-07-12T17:04:09Z","snapshot_observed_at":"2026-08-08T02:33:52.785761Z","submitted_at":"2025-07-02T10:27:59Z","title":"Multi Source COVID-19 Detection via Kernel-Density-based Slice Sampling","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T20:51:56.492987Z"},"links":{"citing_paper":"/paper/2507.01564"},"observation_digest":"sha256:8ac4c4a654e1e346a2e7116160e2ff82f43f46ef163f8ed7fbba9da8897d08b1","observation_id":"bb4fb4e5-d6bf-4284-bda5-51d192c088af","resolution":{"observed_at":"2026-08-06T20:51:58.261815Z","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":"2009.07044","last_updated":"2020-09-20T22:06:43Z","snapshot_observed_at":"2026-08-09T08:22:01.433381Z","submitted_at":"2020-09-13T19:21:40Z","title":"Deep Transparent Prediction through Latent Representation Analysis","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2009.07044","snapshot_observed_at":"2026-08-06T20:51:56.576395Z","title":"Deep transparent prediction through latent repre- sentation analysis","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2507.01564","last_updated":"2025-07-12T17:04:09Z","snapshot_observed_at":"2026-08-08T02:33:52.785761Z","submitted_at":"2025-07-02T10:27:59Z","title":"Multi Source COVID-19 Detection via Kernel-Density-based Slice Sampling","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T20:51:56.576395Z"},"links":{"cited_paper":"/paper/2009.07044","citing_paper":"/paper/2507.01564"},"observation_digest":"sha256:54cf0bdb8b33c5b8bacb7f1dcd28bb7392b9a7faf5944479a217ed8510922e08","observation_id":"15cbb850-ecfa-4c09-b613-736b1692c96a","resolution":{"observed_at":"2026-08-06T20:51:56.576395Z","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":"2026-08-06T20:51:56.664210Z","title":"Transpar- ent adaptation in deep medical image diagnosis","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.01564","last_updated":"2025-07-12T17:04:09Z","snapshot_observed_at":"2026-08-08T02:33:52.785761Z","submitted_at":"2025-07-02T10:27:59Z","title":"Multi Source COVID-19 Detection via Kernel-Density-based Slice Sampling","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T20:51:56.664210Z"},"links":{"citing_paper":"/paper/2507.01564"},"observation_digest":"sha256:524fff083dd522552f4dfa895ac04854ab01d43065aad1458e853dee3eb091d8","observation_id":"3742cde8-2661-4130-b8da-8ed636c9370b","resolution":{"observed_at":"2026-08-06T20:51:56.664210Z","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":"2026-08-06T20:51:58.039242Z","title":"Mia-cov19d: Covid-19 detection through 3-d chest ct image analysis","venue":null,"work_id":"a547c2b1-3abd-401b-b22e-6eadce1736f6","year":2021},"citing_paper":{"arxiv_id":"2507.01564","last_updated":"2025-07-12T17:04:09Z","snapshot_observed_at":"2026-08-08T02:33:52.785761Z","submitted_at":"2025-07-02T10:27:59Z","title":"Multi Source COVID-19 Detection via Kernel-Density-based Slice Sampling","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T20:51:56.749867Z"},"links":{"citing_paper":"/paper/2507.01564"},"observation_digest":"sha256:b5c976b019477b08b25e741542bf290c549eefe9f028ec35fb62bba9e2d7e1bb","observation_id":"daa8df2b-0869-4a63-b832-5a17a43eac0e","resolution":{"observed_at":"2026-08-06T20:51:58.120739Z","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":"2026-08-06T20:51:57.902840Z","title":"Ai-mia: Covid-19 detection and severity analysis through medical imaging","venue":null,"work_id":"1b0ff788-60fc-46ff-84ad-5a8547cd7a7c","year":2022},"citing_paper":{"arxiv_id":"2507.01564","last_updated":"2025-07-12T17:04:09Z","snapshot_observed_at":"2026-08-08T02:33:52.785761Z","submitted_at":"2025-07-02T10:27:59Z","title":"Multi Source COVID-19 Detection via Kernel-Density-based Slice Sampling","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T20:51:56.836477Z"},"links":{"citing_paper":"/paper/2507.01564"},"observation_digest":"sha256:5b0de142c1c3841b60bca115e10f288dffbe138bc8b903c8fa2748febbc8a285","observation_id":"c9b275e3-2974-4f48-aaf2-0fd1c12fb253","resolution":{"observed_at":"2026-08-06T20:51:57.975249Z","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":"2026-08-06T20:51:57.752157Z","title":"Ai-enabled analysis of 3-d ct scans for diagnosis of covid-19 & its severity","venue":null,"work_id":"5ecc0726-2499-4d53-93d8-47a026c53cf3","year":2023},"citing_paper":{"arxiv_id":"2507.01564","last_updated":"2025-07-12T17:04:09Z","snapshot_observed_at":"2026-08-08T02:33:52.785761Z","submitted_at":"2025-07-02T10:27:59Z","title":"Multi Source COVID-19 Detection via Kernel-Density-based Slice Sampling","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T20:51:56.921573Z"},"links":{"citing_paper":"/paper/2507.01564"},"observation_digest":"sha256:e60b5bcde01a449b4eff4a52a0f9aea535fd6ad3d91f4aad2d582037838e6590","observation_id":"4867a0f5-d1c0-4a6e-938f-9f2400595429","resolution":{"observed_at":"2026-08-06T20:51:57.832492Z","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":"2026-08-06T20:51:57.005033Z","title":"A deep neural architecture for harmonizing 3-d input data analysis and decision making in medical imaging","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.01564","last_updated":"2025-07-12T17:04:09Z","snapshot_observed_at":"2026-08-08T02:33:52.785761Z","submitted_at":"2025-07-02T10:27:59Z","title":"Multi Source COVID-19 Detection via Kernel-Density-based Slice Sampling","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T20:51:57.005033Z"},"links":{"citing_paper":"/paper/2507.01564"},"observation_digest":"sha256:69be53492419a13001f4b069441035d6b4fb13ca9fb6ad420d3009bb9e5c3cb4","observation_id":"5f899634-aa01-49b7-b6b8-2e33f0d3801a","resolution":{"observed_at":"2026-08-06T20:51:57.005033Z","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":"2026-08-06T20:51:57.099270Z","title":"Domain adaptation explainability & fairness in ai for medical image analysis: Diagnosis of covid-19 based on 3-d chest ct-scans","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.01564","last_updated":"2025-07-12T17:04:09Z","snapshot_observed_at":"2026-08-08T02:33:52.785761Z","submitted_at":"2025-07-02T10:27:59Z","title":"Multi Source COVID-19 Detection via Kernel-Density-based Slice Sampling","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T20:51:57.099270Z"},"links":{"citing_paper":"/paper/2507.01564"},"observation_digest":"sha256:1729d815a8f65c65a00b62316fa98974de119241cebee34703c1c6a56bb44734","observation_id":"09d5a04c-ec6c-4765-aae7-31cf82ba3e55","resolution":{"observed_at":"2026-08-06T20:51:57.099270Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.15728","last_updated":"2024-07-23T23:53:03Z","snapshot_observed_at":"2026-08-04T01:57:15.770666Z","submitted_at":"2024-07-22T15:31:18Z","title":"SAM2CLIP2SAM: Vision Language Model for Segmentation of 3D CT Scans for Covid-19 Detection","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.15728","snapshot_observed_at":"2026-08-06T20:51:57.159522Z","title":"Sam2clip2sam: Vision language model for segmentation of 3d ct scans for covid-19 detection","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.01564","last_updated":"2025-07-12T17:04:09Z","snapshot_observed_at":"2026-08-08T02:33:52.785761Z","submitted_at":"2025-07-02T10:27:59Z","title":"Multi Source COVID-19 Detection via Kernel-Density-based Slice Sampling","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T20:51:57.159522Z"},"links":{"cited_paper":"/paper/2407.15728","citing_paper":"/paper/2507.01564"},"observation_digest":"sha256:e2f022824848da22fcfccfee52e7c7193cb80c0cb219f32b2bcf53bc93928a3f","observation_id":"3d9e54b3-b31b-4742-8baf-031dcdecbed5","resolution":{"observed_at":"2026-08-06T20:51:57.159522Z","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":"2026-08-06T20:51:57.251459Z","title":"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.01564","last_updated":"2025-07-12T17:04:09Z","snapshot_observed_at":"2026-08-08T02:33:52.785761Z","submitted_at":"2025-07-02T10:27:59Z","title":"Multi Source COVID-19 Detection via Kernel-Density-based Slice Sampling","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T20:51:57.251459Z"},"links":{"citing_paper":"/paper/2507.01564"},"observation_digest":"sha256:252e8349d7aa7b3bdd8c994f86fec50e6b9af2694660500bd7d23bdaba90301d","observation_id":"db3784fa-5c34-4e90-9f06-e55cc0ec56e1","resolution":{"observed_at":"2026-08-06T20:51:57.251459Z","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":"2026-08-06T20:51:57.540858Z","title":"Efficientnet: Rethinking model scaling for convolutional neural networks","venue":null,"work_id":"662f2934-6bd6-4336-895b-be2d47f42c2e","year":2019},"citing_paper":{"arxiv_id":"2507.01564","last_updated":"2025-07-12T17:04:09Z","snapshot_observed_at":"2026-08-08T02:33:52.785761Z","submitted_at":"2025-07-02T10:27:59Z","title":"Multi Source COVID-19 Detection via Kernel-Density-based Slice Sampling","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T20:51:57.398609Z"},"links":{"citing_paper":"/paper/2507.01564"},"observation_digest":"sha256:ec8e046fe774025a49c9813bfc2ca4bf1133f6df637c2c3ae4c672c57bdbf760","observation_id":"6d26b3f3-79ab-4f63-916f-f96d2b4ccb84","resolution":{"observed_at":"2026-08-06T20:51:57.627885Z","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":"2026-08-06T20:51:57.334777Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.01564","last_updated":"2025-07-12T17:04:09Z","snapshot_observed_at":"2026-08-08T02:33:52.785761Z","submitted_at":"2025-07-02T10:27:59Z","title":"Multi Source COVID-19 Detection via Kernel-Density-based Slice Sampling","version":2},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-06T20:51:57.334777Z"},"links":{"citing_paper":"/paper/2507.01564"},"observation_digest":"sha256:74f77bcd3105dcc8569b6f1ad8eabae71617d74643b67ef7e68528df841173da","observation_id":"73d5bfa4-aae6-4e7f-b695-4b142a75d6ff","resolution":{"observed_at":"2026-08-06T20:51:57.334777Z","resolver_source":null,"status":"parse_uncertain"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2507.01564","last_updated":"2025-07-12T17:04:09Z","latest_version":2,"primary_category":"eess.IV","snapshot_observed_at":"2026-08-08T02:33:52.785761Z","submitted_at":"2025-07-02T10:27:59Z","title":"Multi Source COVID-19 Detection via Kernel-Density-based Slice Sampling"},"reference_resolution":{"displayed":20,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":1,"unresolved":7,"verified_exact":0,"verified_fuzzy":12},"total_outbound_references":20},"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 11 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 1 inbound Pith citation observation for arXiv:2507.01564."}