{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:G4D3UTZP7E6YFD3KUZPW23BJA2","short_pith_number":"pith:G4D3UTZP","schema_version":"1.0","canonical_sha256":"3707ba4f2ff93d828f6aa65f6d6c29068f9737d21bd7f37b24ad46e225d9eb96","source":{"kind":"arxiv","id":"2106.07592","version":2},"attestation_state":"computed","paper":{"title":"No more glowing in the dark: How deep learning improves exposure date estimation in thermoluminescence dosimetry","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"physics.med-ph","authors_text":"Evelin Derugin, Florian Mentzel, Hannah Jansen, Jens Weingarten, J\\\"org Walbersloh, Kevin Kr\\\"oninger, Olaf Nackenhorst","submitted_at":"2021-06-14T16:51:50Z","abstract_excerpt":"The time- or temperature-resolved detector signal from a thermoluminescence dosimeter can reveal additional information about circumstances of an exposure to ionizing irradiation. We present studies using deep neural networks to estimate the date of a single irradiation with 12 mSv within a monitoring interval of 42 days from glow curves of novel TL-DOS personal dosimeters developed by the Materialpr\\\"ufungsamt NRW in cooperation with TU Dortmund University. Using a deep convolutional network, the irradiation date can be predicted from raw time-resolved glow curve data with an uncertainty of r"},"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":"2106.07592","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.med-ph","submitted_at":"2021-06-14T16:51:50Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"84273817e1d883695ed0ba7d2d753ae1cd1981adcebe9a7b8ed62328c6a68e00","abstract_canon_sha256":"c6865241f76dcc4d208f58e9d62c220d6719962e95b51f116a8239fb0bebf666"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:22:58.079652Z","signature_b64":"r4+ZgiXSeWZzNcbXWDvHwwHw2V4g5Y1uJT9TGVGeLvSv4dP+DbBeqDtoKCrxd3/I15IqlW9fuphZDgDaQgmcBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3707ba4f2ff93d828f6aa65f6d6c29068f9737d21bd7f37b24ad46e225d9eb96","last_reissued_at":"2026-07-05T03:22:58.079076Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:22:58.079076Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"No more glowing in the dark: How deep learning improves exposure date estimation in thermoluminescence dosimetry","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"physics.med-ph","authors_text":"Evelin Derugin, Florian Mentzel, Hannah Jansen, Jens Weingarten, J\\\"org Walbersloh, Kevin Kr\\\"oninger, Olaf Nackenhorst","submitted_at":"2021-06-14T16:51:50Z","abstract_excerpt":"The time- or temperature-resolved detector signal from a thermoluminescence dosimeter can reveal additional information about circumstances of an exposure to ionizing irradiation. We present studies using deep neural networks to estimate the date of a single irradiation with 12 mSv within a monitoring interval of 42 days from glow curves of novel TL-DOS personal dosimeters developed by the Materialpr\\\"ufungsamt NRW in cooperation with TU Dortmund University. Using a deep convolutional network, the irradiation date can be predicted from raw time-resolved glow curve data with an uncertainty of r"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.07592","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/2106.07592/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":"2106.07592","created_at":"2026-07-05T03:22:58.079135+00:00"},{"alias_kind":"arxiv_version","alias_value":"2106.07592v2","created_at":"2026-07-05T03:22:58.079135+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.07592","created_at":"2026-07-05T03:22:58.079135+00:00"},{"alias_kind":"pith_short_12","alias_value":"G4D3UTZP7E6Y","created_at":"2026-07-05T03:22:58.079135+00:00"},{"alias_kind":"pith_short_16","alias_value":"G4D3UTZP7E6YFD3K","created_at":"2026-07-05T03:22:58.079135+00:00"},{"alias_kind":"pith_short_8","alias_value":"G4D3UTZP","created_at":"2026-07-05T03:22:58.079135+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/G4D3UTZP7E6YFD3KUZPW23BJA2","json":"https://pith.science/pith/G4D3UTZP7E6YFD3KUZPW23BJA2.json","graph_json":"https://pith.science/api/pith-number/G4D3UTZP7E6YFD3KUZPW23BJA2/graph.json","events_json":"https://pith.science/api/pith-number/G4D3UTZP7E6YFD3KUZPW23BJA2/events.json","paper":"https://pith.science/paper/G4D3UTZP"},"agent_actions":{"view_html":"https://pith.science/pith/G4D3UTZP7E6YFD3KUZPW23BJA2","download_json":"https://pith.science/pith/G4D3UTZP7E6YFD3KUZPW23BJA2.json","view_paper":"https://pith.science/paper/G4D3UTZP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2106.07592&json=true","fetch_graph":"https://pith.science/api/pith-number/G4D3UTZP7E6YFD3KUZPW23BJA2/graph.json","fetch_events":"https://pith.science/api/pith-number/G4D3UTZP7E6YFD3KUZPW23BJA2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/G4D3UTZP7E6YFD3KUZPW23BJA2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/G4D3UTZP7E6YFD3KUZPW23BJA2/action/storage_attestation","attest_author":"https://pith.science/pith/G4D3UTZP7E6YFD3KUZPW23BJA2/action/author_attestation","sign_citation":"https://pith.science/pith/G4D3UTZP7E6YFD3KUZPW23BJA2/action/citation_signature","submit_replication":"https://pith.science/pith/G4D3UTZP7E6YFD3KUZPW23BJA2/action/replication_record"}},"created_at":"2026-07-05T03:22:58.079135+00:00","updated_at":"2026-07-05T03:22:58.079135+00:00"}