{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:XRN7JDG5P3UK7NFVCYXSI6BQDH","short_pith_number":"pith:XRN7JDG5","schema_version":"1.0","canonical_sha256":"bc5bf48cdd7ee8afb4b5162f24783019cb74837ff28ef7d3528c2ddb84e154a7","source":{"kind":"arxiv","id":"2606.18146","version":1},"attestation_state":"computed","paper":{"title":"Spatial Disease Mapping and Disparity Detection Using Generative AI: An Amortized Bayesian Learning Framework","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Luca Aiello, Sudipto Banerjee","submitted_at":"2026-06-16T16:45:39Z","abstract_excerpt":"We introduce an amortized Bayesian framework for spatial boundary detection that generalizes posterior inference across areal graphs with varying numbers of regions and diverse adjacency structures. The underlying model couples a Poisson count likelihood with a covariate-driven rule to interrupt smoothing across dissimilar neighboring areas, utilizing a directed acyclic graph autoregressive (DAGAR) prior to capture residual spatial dependence. To approximate the target posterior distribution, a neural engine is trained on simulated maps: a permutation-invariant summary network encodes graph-aw"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2606.18146","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"stat.ME","submitted_at":"2026-06-16T16:45:39Z","cross_cats_sorted":[],"title_canon_sha256":"f2ee7e69112bbf6016bae394fcdacd4c19015887f93b04c4361b34d4a96bbf85","abstract_canon_sha256":"ff6e0ccb664abb207c182e7796c909acbfd145f0bbf4b1b6518e627fca088302"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-19T16:10:49.485632Z","signature_b64":"RxAcW2OIodQfV7zsA1PqcJJNoW7F8uzxKOkiOcK3TO9wM+j8DuGjoxT58nZEPYXJB7a6O5vU66y6swGEvCTeCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bc5bf48cdd7ee8afb4b5162f24783019cb74837ff28ef7d3528c2ddb84e154a7","last_reissued_at":"2026-06-19T16:10:49.485267Z","signature_status":"signed_v1","first_computed_at":"2026-06-19T16:10:49.485267Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Spatial Disease Mapping and Disparity Detection Using Generative AI: An Amortized Bayesian Learning Framework","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Luca Aiello, Sudipto Banerjee","submitted_at":"2026-06-16T16:45:39Z","abstract_excerpt":"We introduce an amortized Bayesian framework for spatial boundary detection that generalizes posterior inference across areal graphs with varying numbers of regions and diverse adjacency structures. The underlying model couples a Poisson count likelihood with a covariate-driven rule to interrupt smoothing across dissimilar neighboring areas, utilizing a directed acyclic graph autoregressive (DAGAR) prior to capture residual spatial dependence. To approximate the target posterior distribution, a neural engine is trained on simulated maps: a permutation-invariant summary network encodes graph-aw"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.18146","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2606.18146/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2606.18146","created_at":"2026-06-19T16:10:49.485343+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.18146v1","created_at":"2026-06-19T16:10:49.485343+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.18146","created_at":"2026-06-19T16:10:49.485343+00:00"},{"alias_kind":"pith_short_12","alias_value":"XRN7JDG5P3UK","created_at":"2026-06-19T16:10:49.485343+00:00"},{"alias_kind":"pith_short_16","alias_value":"XRN7JDG5P3UK7NFV","created_at":"2026-06-19T16:10:49.485343+00:00"},{"alias_kind":"pith_short_8","alias_value":"XRN7JDG5","created_at":"2026-06-19T16:10:49.485343+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XRN7JDG5P3UK7NFVCYXSI6BQDH","json":"https://pith.science/pith/XRN7JDG5P3UK7NFVCYXSI6BQDH.json","graph_json":"https://pith.science/api/pith-number/XRN7JDG5P3UK7NFVCYXSI6BQDH/graph.json","events_json":"https://pith.science/api/pith-number/XRN7JDG5P3UK7NFVCYXSI6BQDH/events.json","paper":"https://pith.science/paper/XRN7JDG5"},"agent_actions":{"view_html":"https://pith.science/pith/XRN7JDG5P3UK7NFVCYXSI6BQDH","download_json":"https://pith.science/pith/XRN7JDG5P3UK7NFVCYXSI6BQDH.json","view_paper":"https://pith.science/paper/XRN7JDG5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.18146&json=true","fetch_graph":"https://pith.science/api/pith-number/XRN7JDG5P3UK7NFVCYXSI6BQDH/graph.json","fetch_events":"https://pith.science/api/pith-number/XRN7JDG5P3UK7NFVCYXSI6BQDH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XRN7JDG5P3UK7NFVCYXSI6BQDH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XRN7JDG5P3UK7NFVCYXSI6BQDH/action/storage_attestation","attest_author":"https://pith.science/pith/XRN7JDG5P3UK7NFVCYXSI6BQDH/action/author_attestation","sign_citation":"https://pith.science/pith/XRN7JDG5P3UK7NFVCYXSI6BQDH/action/citation_signature","submit_replication":"https://pith.science/pith/XRN7JDG5P3UK7NFVCYXSI6BQDH/action/replication_record"}},"created_at":"2026-06-19T16:10:49.485343+00:00","updated_at":"2026-06-19T16:10:49.485343+00:00"}