{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:PG2BWWHG7F7D3PDUAOPQUXC7VY","short_pith_number":"pith:PG2BWWHG","schema_version":"1.0","canonical_sha256":"79b41b58e6f97e3dbc74039f0a5c5fae186ac0d8e1c5f2fb30dffa644433468a","source":{"kind":"arxiv","id":"2512.18769","version":4},"attestation_state":"computed","paper":{"title":"Quantitative mobile gamma-ray spectrometry through Bayesian inference","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["physics.app-ph","physics.comp-ph","physics.data-an","physics.geo-ph"],"primary_cat":"physics.ins-det","authors_text":"Alberto Stabilini, David Breitenmoser, Malgorzata Magdalena Kasprzak, Sabine Mayer","submitted_at":"2025-12-21T15:17:52Z","abstract_excerpt":"Accurate quantitative mapping of gamma-ray sources is critical for applications ranging from radiological emergency response and environmental monitoring to nuclear security and deep space exploration. Here, we show that integrating high-fidelity, platform-dynamic Monte Carlo simulations and Bayesian inference with mobile gamma-ray spectrometry enables rapid and accurate inference of the source mixture, associated source activities, and source locations for both distributed and point-like gamma-ray sources. Validated against laboratory and field assays, our framework quantifies anthropogenic g"},"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":"2512.18769","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.ins-det","submitted_at":"2025-12-21T15:17:52Z","cross_cats_sorted":["physics.app-ph","physics.comp-ph","physics.data-an","physics.geo-ph"],"title_canon_sha256":"400b0c95fc2be212566bacf15d4a138c8216dbea9ba13c7e6938403f3d48198e","abstract_canon_sha256":"77c9db231bdf992f466665151c10cea081b2b76accfb7d6c584a6e655b8bef33"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-29T00:14:01.637640Z","signature_b64":"ezmdPyrZUQnsWrukAXyShas0TkDsqmf5NJWS8tCTLidiJXE2zRH+d6o6c9ShliAhZdefTbEZ8yhM8a1hHtfxDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"79b41b58e6f97e3dbc74039f0a5c5fae186ac0d8e1c5f2fb30dffa644433468a","last_reissued_at":"2026-06-29T00:14:01.637102Z","signature_status":"signed_v1","first_computed_at":"2026-06-29T00:14:01.637102Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Quantitative mobile gamma-ray spectrometry through Bayesian inference","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["physics.app-ph","physics.comp-ph","physics.data-an","physics.geo-ph"],"primary_cat":"physics.ins-det","authors_text":"Alberto Stabilini, David Breitenmoser, Malgorzata Magdalena Kasprzak, Sabine Mayer","submitted_at":"2025-12-21T15:17:52Z","abstract_excerpt":"Accurate quantitative mapping of gamma-ray sources is critical for applications ranging from radiological emergency response and environmental monitoring to nuclear security and deep space exploration. Here, we show that integrating high-fidelity, platform-dynamic Monte Carlo simulations and Bayesian inference with mobile gamma-ray spectrometry enables rapid and accurate inference of the source mixture, associated source activities, and source locations for both distributed and point-like gamma-ray sources. Validated against laboratory and field assays, our framework quantifies anthropogenic g"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2512.18769","kind":"arxiv","version":4},"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/2512.18769/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":"2512.18769","created_at":"2026-06-29T00:14:01.637167+00:00"},{"alias_kind":"arxiv_version","alias_value":"2512.18769v4","created_at":"2026-06-29T00:14:01.637167+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2512.18769","created_at":"2026-06-29T00:14:01.637167+00:00"},{"alias_kind":"pith_short_12","alias_value":"PG2BWWHG7F7D","created_at":"2026-06-29T00:14:01.637167+00:00"},{"alias_kind":"pith_short_16","alias_value":"PG2BWWHG7F7D3PDU","created_at":"2026-06-29T00:14:01.637167+00:00"},{"alias_kind":"pith_short_8","alias_value":"PG2BWWHG","created_at":"2026-06-29T00:14:01.637167+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2603.05719","citing_title":"Unsupervised domain adaptation for radioisotope identification in gamma spectroscopy","ref_index":1,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PG2BWWHG7F7D3PDUAOPQUXC7VY","json":"https://pith.science/pith/PG2BWWHG7F7D3PDUAOPQUXC7VY.json","graph_json":"https://pith.science/api/pith-number/PG2BWWHG7F7D3PDUAOPQUXC7VY/graph.json","events_json":"https://pith.science/api/pith-number/PG2BWWHG7F7D3PDUAOPQUXC7VY/events.json","paper":"https://pith.science/paper/PG2BWWHG"},"agent_actions":{"view_html":"https://pith.science/pith/PG2BWWHG7F7D3PDUAOPQUXC7VY","download_json":"https://pith.science/pith/PG2BWWHG7F7D3PDUAOPQUXC7VY.json","view_paper":"https://pith.science/paper/PG2BWWHG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2512.18769&json=true","fetch_graph":"https://pith.science/api/pith-number/PG2BWWHG7F7D3PDUAOPQUXC7VY/graph.json","fetch_events":"https://pith.science/api/pith-number/PG2BWWHG7F7D3PDUAOPQUXC7VY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PG2BWWHG7F7D3PDUAOPQUXC7VY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PG2BWWHG7F7D3PDUAOPQUXC7VY/action/storage_attestation","attest_author":"https://pith.science/pith/PG2BWWHG7F7D3PDUAOPQUXC7VY/action/author_attestation","sign_citation":"https://pith.science/pith/PG2BWWHG7F7D3PDUAOPQUXC7VY/action/citation_signature","submit_replication":"https://pith.science/pith/PG2BWWHG7F7D3PDUAOPQUXC7VY/action/replication_record"}},"created_at":"2026-06-29T00:14:01.637167+00:00","updated_at":"2026-06-29T00:14:01.637167+00:00"}