{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:ZOA6M6GSIIJ5CCOBTAPUWMMZ54","short_pith_number":"pith:ZOA6M6GS","schema_version":"1.0","canonical_sha256":"cb81e678d24213d109c1981f4b3199ef3cb4a19d0f30714b4d8f995218f9ba30","source":{"kind":"arxiv","id":"1603.03043","version":1},"attestation_state":"computed","paper":{"title":"Sick, the spectroscopic inference crank","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.SR"],"primary_cat":"astro-ph.IM","authors_text":"Andrew R Casey","submitted_at":"2016-03-09T21:00:05Z","abstract_excerpt":"There exists an inordinate amount of spectral data in both public and private astronomical archives which remain severely under-utilised. The lack of reliable open-source tools for analysing large volumes of spectra contributes to this situation, which is poised to worsen as large surveys successively release orders of magnitude more spectra. In this Article I introduce sick, the spectroscopic inference crank, a flexible and fast Bayesian tool for inferring astrophysical parameters from spectra. sick can be used to provide a nearest-neighbour estimate of model parameters, a numerically optimis"},"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":"1603.03043","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.IM","submitted_at":"2016-03-09T21:00:05Z","cross_cats_sorted":["astro-ph.SR"],"title_canon_sha256":"772390728dc4737d4ac56f47e7bb71baf4882f3761a17ae1a266b7993ab1659a","abstract_canon_sha256":"89e2d46456a315f09849503309988ec00a28cf1dc07702ab275aacdb7eb5e12b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:17:40.960771Z","signature_b64":"q+UNADaPNh9KiYSJKQIm0UzkjjX79faJeFESqj3mEnUAPHbo6UjciUwv3MZ083exR7bkzCpRYrh2tTcYRFceAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cb81e678d24213d109c1981f4b3199ef3cb4a19d0f30714b4d8f995218f9ba30","last_reissued_at":"2026-05-18T01:17:40.960003Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:17:40.960003Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Sick, the spectroscopic inference crank","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.SR"],"primary_cat":"astro-ph.IM","authors_text":"Andrew R Casey","submitted_at":"2016-03-09T21:00:05Z","abstract_excerpt":"There exists an inordinate amount of spectral data in both public and private astronomical archives which remain severely under-utilised. The lack of reliable open-source tools for analysing large volumes of spectra contributes to this situation, which is poised to worsen as large surveys successively release orders of magnitude more spectra. In this Article I introduce sick, the spectroscopic inference crank, a flexible and fast Bayesian tool for inferring astrophysical parameters from spectra. sick can be used to provide a nearest-neighbour estimate of model parameters, a numerically optimis"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1603.03043","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":""},"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":"1603.03043","created_at":"2026-05-18T01:17:40.960130+00:00"},{"alias_kind":"arxiv_version","alias_value":"1603.03043v1","created_at":"2026-05-18T01:17:40.960130+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1603.03043","created_at":"2026-05-18T01:17:40.960130+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZOA6M6GSIIJ5","created_at":"2026-05-18T12:30:55.937587+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZOA6M6GSIIJ5CCOB","created_at":"2026-05-18T12:30:55.937587+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZOA6M6GS","created_at":"2026-05-18T12:30:55.937587+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2608.04767","citing_title":"Cepheid metallicities from low-resolution near-infrared spectra: validation against the optical reference scale","ref_index":12,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZOA6M6GSIIJ5CCOBTAPUWMMZ54","json":"https://pith.science/pith/ZOA6M6GSIIJ5CCOBTAPUWMMZ54.json","graph_json":"https://pith.science/api/pith-number/ZOA6M6GSIIJ5CCOBTAPUWMMZ54/graph.json","events_json":"https://pith.science/api/pith-number/ZOA6M6GSIIJ5CCOBTAPUWMMZ54/events.json","paper":"https://pith.science/paper/ZOA6M6GS"},"agent_actions":{"view_html":"https://pith.science/pith/ZOA6M6GSIIJ5CCOBTAPUWMMZ54","download_json":"https://pith.science/pith/ZOA6M6GSIIJ5CCOBTAPUWMMZ54.json","view_paper":"https://pith.science/paper/ZOA6M6GS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1603.03043&json=true","fetch_graph":"https://pith.science/api/pith-number/ZOA6M6GSIIJ5CCOBTAPUWMMZ54/graph.json","fetch_events":"https://pith.science/api/pith-number/ZOA6M6GSIIJ5CCOBTAPUWMMZ54/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZOA6M6GSIIJ5CCOBTAPUWMMZ54/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZOA6M6GSIIJ5CCOBTAPUWMMZ54/action/storage_attestation","attest_author":"https://pith.science/pith/ZOA6M6GSIIJ5CCOBTAPUWMMZ54/action/author_attestation","sign_citation":"https://pith.science/pith/ZOA6M6GSIIJ5CCOBTAPUWMMZ54/action/citation_signature","submit_replication":"https://pith.science/pith/ZOA6M6GSIIJ5CCOBTAPUWMMZ54/action/replication_record"}},"created_at":"2026-05-18T01:17:40.960130+00:00","updated_at":"2026-05-18T01:17:40.960130+00:00"}