{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:AVQSLVQREABUBT2V3ZYXTDDF7Q","short_pith_number":"pith:AVQSLVQR","canonical_record":{"source":{"id":"2605.21782","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2026-05-20T22:22:06Z","cross_cats_sorted":["stat.AP","stat.CO"],"title_canon_sha256":"5fe220cc065d876cd28612e14c9030e78ba20f3b45950ed2a813fb341586b4ed","abstract_canon_sha256":"41d851e01fa352bb90a456204a13e7d8c30d8f89931380f31eaa3c5884c99a7a"},"schema_version":"1.0"},"canonical_sha256":"056125d611200340cf55de71798c65fc164c974872462d6e88df1b5d646735b7","source":{"kind":"arxiv","id":"2605.21782","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.21782","created_at":"2026-05-22T01:03:32Z"},{"alias_kind":"arxiv_version","alias_value":"2605.21782v1","created_at":"2026-05-22T01:03:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.21782","created_at":"2026-05-22T01:03:32Z"},{"alias_kind":"pith_short_12","alias_value":"AVQSLVQREABU","created_at":"2026-05-22T01:03:32Z"},{"alias_kind":"pith_short_16","alias_value":"AVQSLVQREABUBT2V","created_at":"2026-05-22T01:03:32Z"},{"alias_kind":"pith_short_8","alias_value":"AVQSLVQR","created_at":"2026-05-22T01:03:32Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:AVQSLVQREABUBT2V3ZYXTDDF7Q","target":"record","payload":{"canonical_record":{"source":{"id":"2605.21782","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2026-05-20T22:22:06Z","cross_cats_sorted":["stat.AP","stat.CO"],"title_canon_sha256":"5fe220cc065d876cd28612e14c9030e78ba20f3b45950ed2a813fb341586b4ed","abstract_canon_sha256":"41d851e01fa352bb90a456204a13e7d8c30d8f89931380f31eaa3c5884c99a7a"},"schema_version":"1.0"},"canonical_sha256":"056125d611200340cf55de71798c65fc164c974872462d6e88df1b5d646735b7","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-22T01:03:32.317067Z","signature_b64":"yhTjChfaWEpFe4Qij57Gm6rzJafFI3ZYk74koHaRtH+npFwhVTjs1AdSqe/DinGnZnTSNQHE7yvINFA7lQpbAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"056125d611200340cf55de71798c65fc164c974872462d6e88df1b5d646735b7","last_reissued_at":"2026-05-22T01:03:32.316646Z","signature_status":"signed_v1","first_computed_at":"2026-05-22T01:03:32.316646Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.21782","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-22T01:03:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VNWHU8NJ1ykOw2WMal+QRLm5crFmY4AbnVe5HUqghqvlRYAoDw/tkho+fwGkh/9JO38Yg3SXr/SG4a1R7YTVCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T14:22:24.809022Z"},"content_sha256":"9109139c5320f56ab2268998784b84a1f69e41a0543cc5b1ebe9e390b38a0f02","schema_version":"1.0","event_id":"sha256:9109139c5320f56ab2268998784b84a1f69e41a0543cc5b1ebe9e390b38a0f02"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:AVQSLVQREABUBT2V3ZYXTDDF7Q","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Scalable Parametric Item Calibration Engine (SPICE) for Explanatory IRT with Sparse Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP","stat.CO"],"primary_cat":"stat.ME","authors_text":"J.R. Lockwood, Manqian Liao, Steven W. Nydick","submitted_at":"2026-05-20T22:22:06Z","abstract_excerpt":"We describe a Bayesian multidimensional explanatory IRT model, and an associated Markov Chain Monte Carlo (MCMC) estimation procedure and the corresponding development of calibration software, designed for psychometric analyses of large numbers of sparsely-linked persons and items. Such data structures can arise, for example, from adaptive assessments using large banks of automatically generated items with individual test takers receiving a very small proportion of the entire bank. We discuss how our choices for model specification, data structures, and algorithm implementation combine to crea"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.21782","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.21782/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":"2026-05-22T01:03:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vmtTdgIMA3DS8kgOQM3txaUFl/5VfmcGTwLIyZVst8tZ/rmouAmvoWRIn4IIo3SZheAA1dSkli7sjTFk0fnFBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T14:22:24.809617Z"},"content_sha256":"76aca0c8c68888c9f6d8bc9587c21b6e4e8ac25825a1dca8e2699dfb3dd7628a","schema_version":"1.0","event_id":"sha256:76aca0c8c68888c9f6d8bc9587c21b6e4e8ac25825a1dca8e2699dfb3dd7628a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/AVQSLVQREABUBT2V3ZYXTDDF7Q/bundle.json","state_url":"https://pith.science/pith/AVQSLVQREABUBT2V3ZYXTDDF7Q/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/AVQSLVQREABUBT2V3ZYXTDDF7Q/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":"2026-05-26T14:22:24Z","links":{"resolver":"https://pith.science/pith/AVQSLVQREABUBT2V3ZYXTDDF7Q","bundle":"https://pith.science/pith/AVQSLVQREABUBT2V3ZYXTDDF7Q/bundle.json","state":"https://pith.science/pith/AVQSLVQREABUBT2V3ZYXTDDF7Q/state.json","well_known_bundle":"https://pith.science/.well-known/pith/AVQSLVQREABUBT2V3ZYXTDDF7Q/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:AVQSLVQREABUBT2V3ZYXTDDF7Q","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":"41d851e01fa352bb90a456204a13e7d8c30d8f89931380f31eaa3c5884c99a7a","cross_cats_sorted":["stat.AP","stat.CO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2026-05-20T22:22:06Z","title_canon_sha256":"5fe220cc065d876cd28612e14c9030e78ba20f3b45950ed2a813fb341586b4ed"},"schema_version":"1.0","source":{"id":"2605.21782","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.21782","created_at":"2026-05-22T01:03:32Z"},{"alias_kind":"arxiv_version","alias_value":"2605.21782v1","created_at":"2026-05-22T01:03:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.21782","created_at":"2026-05-22T01:03:32Z"},{"alias_kind":"pith_short_12","alias_value":"AVQSLVQREABU","created_at":"2026-05-22T01:03:32Z"},{"alias_kind":"pith_short_16","alias_value":"AVQSLVQREABUBT2V","created_at":"2026-05-22T01:03:32Z"},{"alias_kind":"pith_short_8","alias_value":"AVQSLVQR","created_at":"2026-05-22T01:03:32Z"}],"graph_snapshots":[{"event_id":"sha256:76aca0c8c68888c9f6d8bc9587c21b6e4e8ac25825a1dca8e2699dfb3dd7628a","target":"graph","created_at":"2026-05-22T01:03:32Z","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/2605.21782/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We describe a Bayesian multidimensional explanatory IRT model, and an associated Markov Chain Monte Carlo (MCMC) estimation procedure and the corresponding development of calibration software, designed for psychometric analyses of large numbers of sparsely-linked persons and items. Such data structures can arise, for example, from adaptive assessments using large banks of automatically generated items with individual test takers receiving a very small proportion of the entire bank. We discuss how our choices for model specification, data structures, and algorithm implementation combine to crea","authors_text":"J.R. Lockwood, Manqian Liao, Steven W. Nydick","cross_cats":["stat.AP","stat.CO"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2026-05-20T22:22:06Z","title":"A Scalable Parametric Item Calibration Engine (SPICE) for Explanatory IRT with Sparse Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.21782","kind":"arxiv","version":1},"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":"sha256:9109139c5320f56ab2268998784b84a1f69e41a0543cc5b1ebe9e390b38a0f02","target":"record","created_at":"2026-05-22T01:03:32Z","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":"41d851e01fa352bb90a456204a13e7d8c30d8f89931380f31eaa3c5884c99a7a","cross_cats_sorted":["stat.AP","stat.CO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2026-05-20T22:22:06Z","title_canon_sha256":"5fe220cc065d876cd28612e14c9030e78ba20f3b45950ed2a813fb341586b4ed"},"schema_version":"1.0","source":{"id":"2605.21782","kind":"arxiv","version":1}},"canonical_sha256":"056125d611200340cf55de71798c65fc164c974872462d6e88df1b5d646735b7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"056125d611200340cf55de71798c65fc164c974872462d6e88df1b5d646735b7","first_computed_at":"2026-05-22T01:03:32.316646Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-22T01:03:32.316646Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"yhTjChfaWEpFe4Qij57Gm6rzJafFI3ZYk74koHaRtH+npFwhVTjs1AdSqe/DinGnZnTSNQHE7yvINFA7lQpbAQ==","signature_status":"signed_v1","signed_at":"2026-05-22T01:03:32.317067Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.21782","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9109139c5320f56ab2268998784b84a1f69e41a0543cc5b1ebe9e390b38a0f02","sha256:76aca0c8c68888c9f6d8bc9587c21b6e4e8ac25825a1dca8e2699dfb3dd7628a"],"state_sha256":"9bde875fad8c3ec4735fe67c1a586f6eec9bdf3fe79385886180d5b6f8f7e99e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xpj/eBvXXsJBNy8ABF2kcA9q3cwVZNxc9e6dvCb4TxmmmcDjorhd4zUOB+jgra9WbWUmozStzxfPBU0G2w5tBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T14:22:24.813240Z","bundle_sha256":"f187cf5b1aa2da2dd993e417a749fa7044db8764adc7114d14ce4e67602887be"}}