{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:G6MPSCZ4LNPQPC7FEMSKWQ73UN","short_pith_number":"pith:G6MPSCZ4","schema_version":"1.0","canonical_sha256":"3798f90b3c5b5f078be52324ab43fba363dac1ffe4b2e03112f855b947a03108","source":{"kind":"arxiv","id":"1703.08085","version":4},"attestation_state":"computed","paper":{"title":"Reducing Crowdsourcing to Graphon Estimation, Statistically","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Christina Lee Yu, Devavrat Shah","submitted_at":"2017-03-23T14:29:29Z","abstract_excerpt":"Inferring the correct answers to binary tasks based on multiple noisy answers in an unsupervised manner has emerged as the canonical question for micro-task crowdsourcing or more generally aggregating opinions. In graphon estimation, one is interested in estimating edge intensities or probabilities between nodes using a single snapshot of a graph realization. In the recent literature, there has been exciting development within both of these topics. In the context of crowdsourcing, the key intellectual challenge is to understand whether a given task can be more accurately denoised by aggregatin"},"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":"1703.08085","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-03-23T14:29:29Z","cross_cats_sorted":[],"title_canon_sha256":"ef70f6d8352b9b1cfb247e0bc2b316909b22706b9a122681f22299e68eec30ed","abstract_canon_sha256":"375719e772de3362e5e77f4a5cc90bc378def20bba8561cf28336dbbccf3788a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:39:32.629992Z","signature_b64":"rH72eiN3ArCLwYMBnxcqoJzooGHwuhqpioiJUo8o7Dqg4kR0JS32BcG10iGPZRRsSvmteG5BySnjj5Z5dOYOAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3798f90b3c5b5f078be52324ab43fba363dac1ffe4b2e03112f855b947a03108","last_reissued_at":"2026-05-17T23:39:32.629409Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:39:32.629409Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Reducing Crowdsourcing to Graphon Estimation, Statistically","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Christina Lee Yu, Devavrat Shah","submitted_at":"2017-03-23T14:29:29Z","abstract_excerpt":"Inferring the correct answers to binary tasks based on multiple noisy answers in an unsupervised manner has emerged as the canonical question for micro-task crowdsourcing or more generally aggregating opinions. In graphon estimation, one is interested in estimating edge intensities or probabilities between nodes using a single snapshot of a graph realization. In the recent literature, there has been exciting development within both of these topics. In the context of crowdsourcing, the key intellectual challenge is to understand whether a given task can be more accurately denoised by aggregatin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.08085","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":"1703.08085","created_at":"2026-05-17T23:39:32.629495+00:00"},{"alias_kind":"arxiv_version","alias_value":"1703.08085v4","created_at":"2026-05-17T23:39:32.629495+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.08085","created_at":"2026-05-17T23:39:32.629495+00:00"},{"alias_kind":"pith_short_12","alias_value":"G6MPSCZ4LNPQ","created_at":"2026-05-18T12:31:15.632608+00:00"},{"alias_kind":"pith_short_16","alias_value":"G6MPSCZ4LNPQPC7F","created_at":"2026-05-18T12:31:15.632608+00:00"},{"alias_kind":"pith_short_8","alias_value":"G6MPSCZ4","created_at":"2026-05-18T12:31:15.632608+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/G6MPSCZ4LNPQPC7FEMSKWQ73UN","json":"https://pith.science/pith/G6MPSCZ4LNPQPC7FEMSKWQ73UN.json","graph_json":"https://pith.science/api/pith-number/G6MPSCZ4LNPQPC7FEMSKWQ73UN/graph.json","events_json":"https://pith.science/api/pith-number/G6MPSCZ4LNPQPC7FEMSKWQ73UN/events.json","paper":"https://pith.science/paper/G6MPSCZ4"},"agent_actions":{"view_html":"https://pith.science/pith/G6MPSCZ4LNPQPC7FEMSKWQ73UN","download_json":"https://pith.science/pith/G6MPSCZ4LNPQPC7FEMSKWQ73UN.json","view_paper":"https://pith.science/paper/G6MPSCZ4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1703.08085&json=true","fetch_graph":"https://pith.science/api/pith-number/G6MPSCZ4LNPQPC7FEMSKWQ73UN/graph.json","fetch_events":"https://pith.science/api/pith-number/G6MPSCZ4LNPQPC7FEMSKWQ73UN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/G6MPSCZ4LNPQPC7FEMSKWQ73UN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/G6MPSCZ4LNPQPC7FEMSKWQ73UN/action/storage_attestation","attest_author":"https://pith.science/pith/G6MPSCZ4LNPQPC7FEMSKWQ73UN/action/author_attestation","sign_citation":"https://pith.science/pith/G6MPSCZ4LNPQPC7FEMSKWQ73UN/action/citation_signature","submit_replication":"https://pith.science/pith/G6MPSCZ4LNPQPC7FEMSKWQ73UN/action/replication_record"}},"created_at":"2026-05-17T23:39:32.629495+00:00","updated_at":"2026-05-17T23:39:32.629495+00:00"}