{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:W6NRV5USMAAS23WR7KT6TAZ333","short_pith_number":"pith:W6NRV5US","schema_version":"1.0","canonical_sha256":"b79b1af69260012d6ed1faa7e9833bdeca3cc0276f463f8a699b04b48498235d","source":{"kind":"arxiv","id":"2512.01508","version":2},"attestation_state":"computed","paper":{"title":"A mixture of distributed lag non-linear models to account for spatially heterogeneous exposure-lag-response associations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"stat.ME","authors_text":"Adina Iftimi, \\'Alvaro Briz-Red\\'on, Ana Corber\\'an-Vallet, Carmen \\'I\\~niguez","submitted_at":"2025-12-01T10:34:31Z","abstract_excerpt":"Environmental exposures, such as air pollution and extreme temperatures, have complex effects on human health. These effects are often characterized by non-linear exposure-lag-response relationships and delayed impacts over time. Accurately capturing these dynamics is crucial for informing public health interventions. The Distributed Lag Non-Linear Model (DLNM) is a flexible statistical framework for estimating such effects in epidemiological research. However, standard DLNM implementations typically assume a homogeneous exposure-lag-response association across the study region, overlooking po"},"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":"2512.01508","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2025-12-01T10:34:31Z","cross_cats_sorted":["stat.AP"],"title_canon_sha256":"64d87d2b46a44e1a2aaae2ebf2b9d3390f982120ca001260252383cec9b4a845","abstract_canon_sha256":"8b1db971a5aa1d1eaf7d8b2522a487b1adc5c714879808af2dac9f372d0d237b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-23T03:13:52.148672Z","signature_b64":"+0u3sNx0cXs+AGTr+7H0sm8ptwdYPwMXk7kqT6/aRIVYPHIvC0y37leNpNGg4JNhvcOukuEjJWe/hy2WxO13Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b79b1af69260012d6ed1faa7e9833bdeca3cc0276f463f8a699b04b48498235d","last_reissued_at":"2026-06-23T03:13:52.148179Z","signature_status":"signed_v1","first_computed_at":"2026-06-23T03:13:52.148179Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A mixture of distributed lag non-linear models to account for spatially heterogeneous exposure-lag-response associations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"stat.ME","authors_text":"Adina Iftimi, \\'Alvaro Briz-Red\\'on, Ana Corber\\'an-Vallet, Carmen \\'I\\~niguez","submitted_at":"2025-12-01T10:34:31Z","abstract_excerpt":"Environmental exposures, such as air pollution and extreme temperatures, have complex effects on human health. These effects are often characterized by non-linear exposure-lag-response relationships and delayed impacts over time. Accurately capturing these dynamics is crucial for informing public health interventions. The Distributed Lag Non-Linear Model (DLNM) is a flexible statistical framework for estimating such effects in epidemiological research. However, standard DLNM implementations typically assume a homogeneous exposure-lag-response association across the study region, overlooking po"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2512.01508","kind":"arxiv","version":2},"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/2512.01508/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":"2512.01508","created_at":"2026-06-23T03:13:52.148241+00:00"},{"alias_kind":"arxiv_version","alias_value":"2512.01508v2","created_at":"2026-06-23T03:13:52.148241+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2512.01508","created_at":"2026-06-23T03:13:52.148241+00:00"},{"alias_kind":"pith_short_12","alias_value":"W6NRV5USMAAS","created_at":"2026-06-23T03:13:52.148241+00:00"},{"alias_kind":"pith_short_16","alias_value":"W6NRV5USMAAS23WR","created_at":"2026-06-23T03:13:52.148241+00:00"},{"alias_kind":"pith_short_8","alias_value":"W6NRV5US","created_at":"2026-06-23T03:13:52.148241+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/W6NRV5USMAAS23WR7KT6TAZ333","json":"https://pith.science/pith/W6NRV5USMAAS23WR7KT6TAZ333.json","graph_json":"https://pith.science/api/pith-number/W6NRV5USMAAS23WR7KT6TAZ333/graph.json","events_json":"https://pith.science/api/pith-number/W6NRV5USMAAS23WR7KT6TAZ333/events.json","paper":"https://pith.science/paper/W6NRV5US"},"agent_actions":{"view_html":"https://pith.science/pith/W6NRV5USMAAS23WR7KT6TAZ333","download_json":"https://pith.science/pith/W6NRV5USMAAS23WR7KT6TAZ333.json","view_paper":"https://pith.science/paper/W6NRV5US","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2512.01508&json=true","fetch_graph":"https://pith.science/api/pith-number/W6NRV5USMAAS23WR7KT6TAZ333/graph.json","fetch_events":"https://pith.science/api/pith-number/W6NRV5USMAAS23WR7KT6TAZ333/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/W6NRV5USMAAS23WR7KT6TAZ333/action/timestamp_anchor","attest_storage":"https://pith.science/pith/W6NRV5USMAAS23WR7KT6TAZ333/action/storage_attestation","attest_author":"https://pith.science/pith/W6NRV5USMAAS23WR7KT6TAZ333/action/author_attestation","sign_citation":"https://pith.science/pith/W6NRV5USMAAS23WR7KT6TAZ333/action/citation_signature","submit_replication":"https://pith.science/pith/W6NRV5USMAAS23WR7KT6TAZ333/action/replication_record"}},"created_at":"2026-06-23T03:13:52.148241+00:00","updated_at":"2026-06-23T03:13:52.148241+00:00"}