{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:M3BX24IGVWRIEMUTF4S5KBQR64","short_pith_number":"pith:M3BX24IG","schema_version":"1.0","canonical_sha256":"66c37d7106ada28232932f25d50611f71d7df020b279c1e0bc19da4769a80193","source":{"kind":"arxiv","id":"1406.7863","version":4},"attestation_state":"computed","paper":{"title":"A Dynamic Approach to Linear Statistical Calibration with an Application in Microwave Radiometry","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.CO","authors_text":"Derick L. Rivers, Edward L. Boone","submitted_at":"2014-06-30T19:29:46Z","abstract_excerpt":"The problem of statistical calibration of a measuring instrument can be framed both in a statistical context as well as in an engineering context. In the first, the problem is dealt with by distinguishing between the 'classical' approach and the 'inverse' regression approach. Both of these models are static models and are used to estimate exact measurements from measurements that are affected by error. In the engineering context, the variables of interest are considered to be taken at the time at which you observe it. The Bayesian time series analysis method of Dynamic Linear Models (DLM) can "},"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":"1406.7863","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2014-06-30T19:29:46Z","cross_cats_sorted":[],"title_canon_sha256":"35e2d862e0bb7ed917022bf9c4754c78f261e3cfaef9d215fb5bf3c4c77a3e01","abstract_canon_sha256":"1ebcdd07b69bc6f604a958e0d6a76ed9c73042bde37bcc44177276c47b8f7b8c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:47:43.592212Z","signature_b64":"+4gflzozpzSG1kZqtAJpHIfKmgBlPnWGWye5djNKymhvbi1xE4gLZJZo226sB9ETm9nyP2i0DxoetxFEuy/LCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"66c37d7106ada28232932f25d50611f71d7df020b279c1e0bc19da4769a80193","last_reissued_at":"2026-05-18T02:47:43.591423Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:47:43.591423Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Dynamic Approach to Linear Statistical Calibration with an Application in Microwave Radiometry","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.CO","authors_text":"Derick L. Rivers, Edward L. Boone","submitted_at":"2014-06-30T19:29:46Z","abstract_excerpt":"The problem of statistical calibration of a measuring instrument can be framed both in a statistical context as well as in an engineering context. In the first, the problem is dealt with by distinguishing between the 'classical' approach and the 'inverse' regression approach. Both of these models are static models and are used to estimate exact measurements from measurements that are affected by error. In the engineering context, the variables of interest are considered to be taken at the time at which you observe it. The Bayesian time series analysis method of Dynamic Linear Models (DLM) can "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1406.7863","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":""},"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":"1406.7863","created_at":"2026-05-18T02:47:43.591547+00:00"},{"alias_kind":"arxiv_version","alias_value":"1406.7863v4","created_at":"2026-05-18T02:47:43.591547+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1406.7863","created_at":"2026-05-18T02:47:43.591547+00:00"},{"alias_kind":"pith_short_12","alias_value":"M3BX24IGVWRI","created_at":"2026-05-18T12:28:38.356838+00:00"},{"alias_kind":"pith_short_16","alias_value":"M3BX24IGVWRIEMUT","created_at":"2026-05-18T12:28:38.356838+00:00"},{"alias_kind":"pith_short_8","alias_value":"M3BX24IG","created_at":"2026-05-18T12:28:38.356838+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/M3BX24IGVWRIEMUTF4S5KBQR64","json":"https://pith.science/pith/M3BX24IGVWRIEMUTF4S5KBQR64.json","graph_json":"https://pith.science/api/pith-number/M3BX24IGVWRIEMUTF4S5KBQR64/graph.json","events_json":"https://pith.science/api/pith-number/M3BX24IGVWRIEMUTF4S5KBQR64/events.json","paper":"https://pith.science/paper/M3BX24IG"},"agent_actions":{"view_html":"https://pith.science/pith/M3BX24IGVWRIEMUTF4S5KBQR64","download_json":"https://pith.science/pith/M3BX24IGVWRIEMUTF4S5KBQR64.json","view_paper":"https://pith.science/paper/M3BX24IG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1406.7863&json=true","fetch_graph":"https://pith.science/api/pith-number/M3BX24IGVWRIEMUTF4S5KBQR64/graph.json","fetch_events":"https://pith.science/api/pith-number/M3BX24IGVWRIEMUTF4S5KBQR64/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/M3BX24IGVWRIEMUTF4S5KBQR64/action/timestamp_anchor","attest_storage":"https://pith.science/pith/M3BX24IGVWRIEMUTF4S5KBQR64/action/storage_attestation","attest_author":"https://pith.science/pith/M3BX24IGVWRIEMUTF4S5KBQR64/action/author_attestation","sign_citation":"https://pith.science/pith/M3BX24IGVWRIEMUTF4S5KBQR64/action/citation_signature","submit_replication":"https://pith.science/pith/M3BX24IGVWRIEMUTF4S5KBQR64/action/replication_record"}},"created_at":"2026-05-18T02:47:43.591547+00:00","updated_at":"2026-05-18T02:47:43.591547+00:00"}