{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:HTFR2C3AN46V524OND7WQ7EGUY","short_pith_number":"pith:HTFR2C3A","schema_version":"1.0","canonical_sha256":"3ccb1d0b606f3d5eeb8e68ff687c86a62516508eb82c24a11521539fce325083","source":{"kind":"arxiv","id":"2008.06131","version":5},"attestation_state":"computed","paper":{"title":"Sequential Monte Carlo for Sampling Balanced and Compact Redistricting Plans","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CY","math.PR"],"primary_cat":"stat.AP","authors_text":"Cory McCartan, Kosuke Imai","submitted_at":"2020-08-13T23:26:34Z","abstract_excerpt":"Random sampling of graph partitions under constraints has become a popular tool for evaluating legislative redistricting plans. Analysts detect partisan gerrymandering by comparing a proposed redistricting plan with an ensemble of sampled alternative plans. For successful application, sampling methods must scale to maps with a moderate or large number of districts, incorporate realistic legal constraints, and accurately and efficiently sample from a selected target distribution. Unfortunately, most existing methods struggle in at least one of these areas. We present a new Sequential Monte Carl"},"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":"2008.06131","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2020-08-13T23:26:34Z","cross_cats_sorted":["cs.CY","math.PR"],"title_canon_sha256":"bafaf3949537cc289123387c6dc1a82f56f92e5fb955751a70563ab3b4faab0b","abstract_canon_sha256":"064ba5ad5f84c5c1abc6b01d96ba68a4b90e7c9cbecd4bf062b286d63d9bf499"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:10:18.973683Z","signature_b64":"Ld4oX2Sj80IJzs9Rf97YhAPebZwDLw0h2s4hlngM1GcFqWxr6dZ9fG4k2Diuz4rNYQFKzNL+Xx7KPQfDAx3QBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3ccb1d0b606f3d5eeb8e68ff687c86a62516508eb82c24a11521539fce325083","last_reissued_at":"2026-07-05T07:10:18.973238Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:10:18.973238Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Sequential Monte Carlo for Sampling Balanced and Compact Redistricting Plans","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CY","math.PR"],"primary_cat":"stat.AP","authors_text":"Cory McCartan, Kosuke Imai","submitted_at":"2020-08-13T23:26:34Z","abstract_excerpt":"Random sampling of graph partitions under constraints has become a popular tool for evaluating legislative redistricting plans. Analysts detect partisan gerrymandering by comparing a proposed redistricting plan with an ensemble of sampled alternative plans. For successful application, sampling methods must scale to maps with a moderate or large number of districts, incorporate realistic legal constraints, and accurately and efficiently sample from a selected target distribution. Unfortunately, most existing methods struggle in at least one of these areas. We present a new Sequential Monte Carl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2008.06131","kind":"arxiv","version":5},"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/2008.06131/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":"2008.06131","created_at":"2026-07-05T07:10:18.973294+00:00"},{"alias_kind":"arxiv_version","alias_value":"2008.06131v5","created_at":"2026-07-05T07:10:18.973294+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2008.06131","created_at":"2026-07-05T07:10:18.973294+00:00"},{"alias_kind":"pith_short_12","alias_value":"HTFR2C3AN46V","created_at":"2026-07-05T07:10:18.973294+00:00"},{"alias_kind":"pith_short_16","alias_value":"HTFR2C3AN46V524O","created_at":"2026-07-05T07:10:18.973294+00:00"},{"alias_kind":"pith_short_8","alias_value":"HTFR2C3A","created_at":"2026-07-05T07:10:18.973294+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.21326","citing_title":"Parameter Effects in ReCom Ensembles","ref_index":12,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HTFR2C3AN46V524OND7WQ7EGUY","json":"https://pith.science/pith/HTFR2C3AN46V524OND7WQ7EGUY.json","graph_json":"https://pith.science/api/pith-number/HTFR2C3AN46V524OND7WQ7EGUY/graph.json","events_json":"https://pith.science/api/pith-number/HTFR2C3AN46V524OND7WQ7EGUY/events.json","paper":"https://pith.science/paper/HTFR2C3A"},"agent_actions":{"view_html":"https://pith.science/pith/HTFR2C3AN46V524OND7WQ7EGUY","download_json":"https://pith.science/pith/HTFR2C3AN46V524OND7WQ7EGUY.json","view_paper":"https://pith.science/paper/HTFR2C3A","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2008.06131&json=true","fetch_graph":"https://pith.science/api/pith-number/HTFR2C3AN46V524OND7WQ7EGUY/graph.json","fetch_events":"https://pith.science/api/pith-number/HTFR2C3AN46V524OND7WQ7EGUY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HTFR2C3AN46V524OND7WQ7EGUY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HTFR2C3AN46V524OND7WQ7EGUY/action/storage_attestation","attest_author":"https://pith.science/pith/HTFR2C3AN46V524OND7WQ7EGUY/action/author_attestation","sign_citation":"https://pith.science/pith/HTFR2C3AN46V524OND7WQ7EGUY/action/citation_signature","submit_replication":"https://pith.science/pith/HTFR2C3AN46V524OND7WQ7EGUY/action/replication_record"}},"created_at":"2026-07-05T07:10:18.973294+00:00","updated_at":"2026-07-05T07:10:18.973294+00:00"}