{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:LKNIF5BHQ4HYYR6VNEZ7S745RA","short_pith_number":"pith:LKNIF5BH","canonical_record":{"source":{"id":"2603.17663","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2026-03-18T12:28:55Z","cross_cats_sorted":[],"title_canon_sha256":"39440535928eb71efa3757fa240f0618287d18c163273e8a10cbc0f5d016b887","abstract_canon_sha256":"05608c2b72a88ae14ce144e5d7138cbf352eafcc570ee40f5fd098549a526c3a"},"schema_version":"1.0"},"canonical_sha256":"5a9a82f427870f8c47d56933f97f9d8813f921ed333e23bdbd74c1b56596198e","source":{"kind":"arxiv","id":"2603.17663","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2603.17663","created_at":"2026-07-14T01:20:56Z"},{"alias_kind":"arxiv_version","alias_value":"2603.17663v2","created_at":"2026-07-14T01:20:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2603.17663","created_at":"2026-07-14T01:20:56Z"},{"alias_kind":"pith_short_12","alias_value":"LKNIF5BHQ4HY","created_at":"2026-07-14T01:20:56Z"},{"alias_kind":"pith_short_16","alias_value":"LKNIF5BHQ4HYYR6V","created_at":"2026-07-14T01:20:56Z"},{"alias_kind":"pith_short_8","alias_value":"LKNIF5BH","created_at":"2026-07-14T01:20:56Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:LKNIF5BHQ4HYYR6VNEZ7S745RA","target":"record","payload":{"canonical_record":{"source":{"id":"2603.17663","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2026-03-18T12:28:55Z","cross_cats_sorted":[],"title_canon_sha256":"39440535928eb71efa3757fa240f0618287d18c163273e8a10cbc0f5d016b887","abstract_canon_sha256":"05608c2b72a88ae14ce144e5d7138cbf352eafcc570ee40f5fd098549a526c3a"},"schema_version":"1.0"},"canonical_sha256":"5a9a82f427870f8c47d56933f97f9d8813f921ed333e23bdbd74c1b56596198e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-14T01:20:56.644356Z","signature_b64":"52Et35jhnJ9/av3qBkvPFa0Q07cUNt2tCTokVSjiSKI0wqnjvhAoviZ67QvNXnoDoCjJSOIcFGh57up3W+5LBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5a9a82f427870f8c47d56933f97f9d8813f921ed333e23bdbd74c1b56596198e","last_reissued_at":"2026-07-14T01:20:56.643470Z","signature_status":"signed_v1","first_computed_at":"2026-07-14T01:20:56.643470Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2603.17663","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-14T01:20:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DcwVjmdTrFvXTsvAVlDhJGeqfA3yJxLyMRozQsJiQYgM5Utnp5btPrikZTFOw2TIDyPFRrwCzPJTuu0MK7XRAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T16:14:21.884765Z"},"content_sha256":"b51a8c05641c9144cdb8269cad08bb08225ee5986c96e6aaee5c9b1c9dea8193","schema_version":"1.0","event_id":"sha256:b51a8c05641c9144cdb8269cad08bb08225ee5986c96e6aaee5c9b1c9dea8193"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:LKNIF5BHQ4HYYR6VNEZ7S745RA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"More with Less -- Bethel Allocation and Precision-Preserving Sample Size Reduction via Hierarchical Bayes Modelling","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Siu-Ming Tam","submitted_at":"2026-03-18T12:28:55Z","abstract_excerpt":"Statistical offices face a familiar and intensifying dilemma: rising demand for detailed regional and domain-level estimates under budgets that are fixed or shrinking. National statistical offices (NSOs) either ignore the problem of optimal sample allocation for multiple target variables when designing a multi-purpose survey, or address it incorrectly - relying on ad hoc approaches such as computing Neyman allocations separately per variable and taking the element-wise maximum, a practice that simultaneously wastes budget and fails to guarantee precision across all domains. This paper presents"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2603.17663","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/2603.17663/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-14T01:20:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dQsjIgUoCNgOkzuFoyOjtF0qwruNoqmk6BzOGlJ4EZXV0v9tj2MP1ko3twk9WWHlfo+zyI5MacdvDCZ0jim0DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T16:14:21.885548Z"},"content_sha256":"2b69c1a07e74665d32903b8b90a6a3b328365600a5f2179c60bdfa99a62f334b","schema_version":"1.0","event_id":"sha256:2b69c1a07e74665d32903b8b90a6a3b328365600a5f2179c60bdfa99a62f334b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LKNIF5BHQ4HYYR6VNEZ7S745RA/bundle.json","state_url":"https://pith.science/pith/LKNIF5BHQ4HYYR6VNEZ7S745RA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LKNIF5BHQ4HYYR6VNEZ7S745RA/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-04T16:14:21Z","links":{"resolver":"https://pith.science/pith/LKNIF5BHQ4HYYR6VNEZ7S745RA","bundle":"https://pith.science/pith/LKNIF5BHQ4HYYR6VNEZ7S745RA/bundle.json","state":"https://pith.science/pith/LKNIF5BHQ4HYYR6VNEZ7S745RA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LKNIF5BHQ4HYYR6VNEZ7S745RA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:LKNIF5BHQ4HYYR6VNEZ7S745RA","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"05608c2b72a88ae14ce144e5d7138cbf352eafcc570ee40f5fd098549a526c3a","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2026-03-18T12:28:55Z","title_canon_sha256":"39440535928eb71efa3757fa240f0618287d18c163273e8a10cbc0f5d016b887"},"schema_version":"1.0","source":{"id":"2603.17663","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2603.17663","created_at":"2026-07-14T01:20:56Z"},{"alias_kind":"arxiv_version","alias_value":"2603.17663v2","created_at":"2026-07-14T01:20:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2603.17663","created_at":"2026-07-14T01:20:56Z"},{"alias_kind":"pith_short_12","alias_value":"LKNIF5BHQ4HY","created_at":"2026-07-14T01:20:56Z"},{"alias_kind":"pith_short_16","alias_value":"LKNIF5BHQ4HYYR6V","created_at":"2026-07-14T01:20:56Z"},{"alias_kind":"pith_short_8","alias_value":"LKNIF5BH","created_at":"2026-07-14T01:20:56Z"}],"graph_snapshots":[{"event_id":"sha256:2b69c1a07e74665d32903b8b90a6a3b328365600a5f2179c60bdfa99a62f334b","target":"graph","created_at":"2026-07-14T01:20:56Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2603.17663/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Statistical offices face a familiar and intensifying dilemma: rising demand for detailed regional and domain-level estimates under budgets that are fixed or shrinking. National statistical offices (NSOs) either ignore the problem of optimal sample allocation for multiple target variables when designing a multi-purpose survey, or address it incorrectly - relying on ad hoc approaches such as computing Neyman allocations separately per variable and taking the element-wise maximum, a practice that simultaneously wastes budget and fails to guarantee precision across all domains. This paper presents","authors_text":"Siu-Ming Tam","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2026-03-18T12:28:55Z","title":"More with Less -- Bethel Allocation and Precision-Preserving Sample Size Reduction via Hierarchical Bayes Modelling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2603.17663","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:b51a8c05641c9144cdb8269cad08bb08225ee5986c96e6aaee5c9b1c9dea8193","target":"record","created_at":"2026-07-14T01:20:56Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"05608c2b72a88ae14ce144e5d7138cbf352eafcc570ee40f5fd098549a526c3a","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2026-03-18T12:28:55Z","title_canon_sha256":"39440535928eb71efa3757fa240f0618287d18c163273e8a10cbc0f5d016b887"},"schema_version":"1.0","source":{"id":"2603.17663","kind":"arxiv","version":2}},"canonical_sha256":"5a9a82f427870f8c47d56933f97f9d8813f921ed333e23bdbd74c1b56596198e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5a9a82f427870f8c47d56933f97f9d8813f921ed333e23bdbd74c1b56596198e","first_computed_at":"2026-07-14T01:20:56.643470Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-14T01:20:56.643470Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"52Et35jhnJ9/av3qBkvPFa0Q07cUNt2tCTokVSjiSKI0wqnjvhAoviZ67QvNXnoDoCjJSOIcFGh57up3W+5LBw==","signature_status":"signed_v1","signed_at":"2026-07-14T01:20:56.644356Z","signed_message":"canonical_sha256_bytes"},"source_id":"2603.17663","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b51a8c05641c9144cdb8269cad08bb08225ee5986c96e6aaee5c9b1c9dea8193","sha256:2b69c1a07e74665d32903b8b90a6a3b328365600a5f2179c60bdfa99a62f334b"],"state_sha256":"80e0edfa462678a8478eb18dd9aa622886975bb40f74fd51fccdfce40e6e5d28"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eVl187Tuk8FCzJxsVs83X6zHmUw2ynxy8bvDNgyWwq+UGZA9UslaqT/Tfg8SC6b/+6cIvG37oIDjKm5OaJQMBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T16:14:21.896442Z","bundle_sha256":"da5142c9d50b28d1c854b54efa9a65da82aacf173143e148b359761b3a42c6cb"}}