{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:DAIV7H6Z54MTOALV4BNBKGI7MR","short_pith_number":"pith:DAIV7H6Z","schema_version":"1.0","canonical_sha256":"18115f9fd9ef19370175e05a15191f645205dcb655bf8647df10f6f2c15dce1d","source":{"kind":"arxiv","id":"2407.05322","version":4},"attestation_state":"computed","paper":{"title":"Correlated Systematic Uncertainties and Errors-on-Errors in Measurement Combinations: Methodology and Application to the 7-8 TeV ATLAS-CMS Top Quark Mass Combination","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"hep-ex","authors_text":"Enzo Canonero, Glen Cowan","submitted_at":"2024-07-07T10:17:40Z","abstract_excerpt":"The Gamma Variance Model (GVM) is a statistical model that incorporates uncertainties in the assignment of systematic errors (informally called errors-on-errors). The model is of particular use in analyses that combine the results of several measurements. In the past, combinations have been carried out using two alternative approaches: the Best Linear Unbiased Estimator (BLUE) method or what we will call the nuisance-parameter method. In this paper we derive useful relations that allow one to connect the BLUE and nuisance-parameter methods when the correlations induced by systematic uncertaint"},"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":"2407.05322","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"hep-ex","submitted_at":"2024-07-07T10:17:40Z","cross_cats_sorted":[],"title_canon_sha256":"e262ca0dec4c0a09bf0ff2c39f3c7574e8a7291ce5d6c8ed4d4ef7f35c87fb36","abstract_canon_sha256":"d8f3caf4488351d7a10dadaf5dd80e4757b623b31748f86c92af592ac9819f38"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:31:24.771611Z","signature_b64":"zG+dRAwRoQEs+CiBl2zpif0CgvBuf2xdnAR9w/HQW7Eio4DvEFdF5Ciz25eBGEeoyAz8YDcT7xLhuPcRZU5GDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"18115f9fd9ef19370175e05a15191f645205dcb655bf8647df10f6f2c15dce1d","last_reissued_at":"2026-07-05T11:31:24.770864Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:31:24.770864Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Correlated Systematic Uncertainties and Errors-on-Errors in Measurement Combinations: Methodology and Application to the 7-8 TeV ATLAS-CMS Top Quark Mass Combination","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"hep-ex","authors_text":"Enzo Canonero, Glen Cowan","submitted_at":"2024-07-07T10:17:40Z","abstract_excerpt":"The Gamma Variance Model (GVM) is a statistical model that incorporates uncertainties in the assignment of systematic errors (informally called errors-on-errors). The model is of particular use in analyses that combine the results of several measurements. In the past, combinations have been carried out using two alternative approaches: the Best Linear Unbiased Estimator (BLUE) method or what we will call the nuisance-parameter method. In this paper we derive useful relations that allow one to connect the BLUE and nuisance-parameter methods when the correlations induced by systematic uncertaint"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.05322","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/2407.05322/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":"2407.05322","created_at":"2026-07-05T11:31:24.770951+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.05322v4","created_at":"2026-07-05T11:31:24.770951+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.05322","created_at":"2026-07-05T11:31:24.770951+00:00"},{"alias_kind":"pith_short_12","alias_value":"DAIV7H6Z54MT","created_at":"2026-07-05T11:31:24.770951+00:00"},{"alias_kind":"pith_short_16","alias_value":"DAIV7H6Z54MTOALV","created_at":"2026-07-05T11:31:24.770951+00:00"},{"alias_kind":"pith_short_8","alias_value":"DAIV7H6Z","created_at":"2026-07-05T11:31:24.770951+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/DAIV7H6Z54MTOALV4BNBKGI7MR","json":"https://pith.science/pith/DAIV7H6Z54MTOALV4BNBKGI7MR.json","graph_json":"https://pith.science/api/pith-number/DAIV7H6Z54MTOALV4BNBKGI7MR/graph.json","events_json":"https://pith.science/api/pith-number/DAIV7H6Z54MTOALV4BNBKGI7MR/events.json","paper":"https://pith.science/paper/DAIV7H6Z"},"agent_actions":{"view_html":"https://pith.science/pith/DAIV7H6Z54MTOALV4BNBKGI7MR","download_json":"https://pith.science/pith/DAIV7H6Z54MTOALV4BNBKGI7MR.json","view_paper":"https://pith.science/paper/DAIV7H6Z","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.05322&json=true","fetch_graph":"https://pith.science/api/pith-number/DAIV7H6Z54MTOALV4BNBKGI7MR/graph.json","fetch_events":"https://pith.science/api/pith-number/DAIV7H6Z54MTOALV4BNBKGI7MR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DAIV7H6Z54MTOALV4BNBKGI7MR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DAIV7H6Z54MTOALV4BNBKGI7MR/action/storage_attestation","attest_author":"https://pith.science/pith/DAIV7H6Z54MTOALV4BNBKGI7MR/action/author_attestation","sign_citation":"https://pith.science/pith/DAIV7H6Z54MTOALV4BNBKGI7MR/action/citation_signature","submit_replication":"https://pith.science/pith/DAIV7H6Z54MTOALV4BNBKGI7MR/action/replication_record"}},"created_at":"2026-07-05T11:31:24.770951+00:00","updated_at":"2026-07-05T11:31:24.770951+00:00"}