{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:NHZENVMMDPQPYZJ4VX7YRORIO3","short_pith_number":"pith:NHZENVMM","schema_version":"1.0","canonical_sha256":"69f246d58c1be0fc653cadff88ba2876dc60e348abaf874a4c3876fc2ab73805","source":{"kind":"arxiv","id":"2303.17731","version":1},"attestation_state":"computed","paper":{"title":"$\\beta^{4}$-IRT: A New $\\beta^{3}$-IRT with Enhanced Discrimination Estimation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Eufrasio A. Lima Neto, Jessica T.S. Reinaldo, Manuel Ferreira-Junior, Ricardo B.C. Prudencio, Telmo M. Silva Filho","submitted_at":"2023-03-30T22:13:11Z","abstract_excerpt":"Item response theory aims to estimate respondent's latent skills from their responses in tests composed of items with different levels of difficulty. Several models of item response theory have been proposed for different types of tasks, such as binary or probabilistic responses, response time, multiple responses, among others. In this paper, we propose a new version of $\\beta^3$-IRT, called $\\beta^{4}$-IRT, which uses the gradient descent method to estimate the model parameters. In $\\beta^3$-IRT, abilities and difficulties are bounded, thus we employ link functions in order to turn $\\beta^{4}"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2303.17731","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-03-30T22:13:11Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"79e34cba9a8e3ce7027a15409bf88bc35aa95b12557ccfd0a330effc3c909948","abstract_canon_sha256":"e6bde1615007d33400a737d954f5b97eebe098162df9b5c2346402b0a644dce9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:56:45.090557Z","signature_b64":"oHy6cSScZ23P6SmKqfbktF0xb2l+7IWh8EjcoqBzrsfJGN905oq2EBoXFIIK3Xrhg7NswX9dDUDW3kbL+LSgDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"69f246d58c1be0fc653cadff88ba2876dc60e348abaf874a4c3876fc2ab73805","last_reissued_at":"2026-07-05T05:56:45.090193Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:56:45.090193Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"$\\beta^{4}$-IRT: A New $\\beta^{3}$-IRT with Enhanced Discrimination Estimation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Eufrasio A. Lima Neto, Jessica T.S. Reinaldo, Manuel Ferreira-Junior, Ricardo B.C. Prudencio, Telmo M. Silva Filho","submitted_at":"2023-03-30T22:13:11Z","abstract_excerpt":"Item response theory aims to estimate respondent's latent skills from their responses in tests composed of items with different levels of difficulty. Several models of item response theory have been proposed for different types of tasks, such as binary or probabilistic responses, response time, multiple responses, among others. In this paper, we propose a new version of $\\beta^3$-IRT, called $\\beta^{4}$-IRT, which uses the gradient descent method to estimate the model parameters. In $\\beta^3$-IRT, abilities and difficulties are bounded, thus we employ link functions in order to turn $\\beta^{4}"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.17731","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/2303.17731/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":"2303.17731","created_at":"2026-07-05T05:56:45.090249+00:00"},{"alias_kind":"arxiv_version","alias_value":"2303.17731v1","created_at":"2026-07-05T05:56:45.090249+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.17731","created_at":"2026-07-05T05:56:45.090249+00:00"},{"alias_kind":"pith_short_12","alias_value":"NHZENVMMDPQP","created_at":"2026-07-05T05:56:45.090249+00:00"},{"alias_kind":"pith_short_16","alias_value":"NHZENVMMDPQPYZJ4","created_at":"2026-07-05T05:56:45.090249+00:00"},{"alias_kind":"pith_short_8","alias_value":"NHZENVMM","created_at":"2026-07-05T05:56:45.090249+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/NHZENVMMDPQPYZJ4VX7YRORIO3","json":"https://pith.science/pith/NHZENVMMDPQPYZJ4VX7YRORIO3.json","graph_json":"https://pith.science/api/pith-number/NHZENVMMDPQPYZJ4VX7YRORIO3/graph.json","events_json":"https://pith.science/api/pith-number/NHZENVMMDPQPYZJ4VX7YRORIO3/events.json","paper":"https://pith.science/paper/NHZENVMM"},"agent_actions":{"view_html":"https://pith.science/pith/NHZENVMMDPQPYZJ4VX7YRORIO3","download_json":"https://pith.science/pith/NHZENVMMDPQPYZJ4VX7YRORIO3.json","view_paper":"https://pith.science/paper/NHZENVMM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2303.17731&json=true","fetch_graph":"https://pith.science/api/pith-number/NHZENVMMDPQPYZJ4VX7YRORIO3/graph.json","fetch_events":"https://pith.science/api/pith-number/NHZENVMMDPQPYZJ4VX7YRORIO3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NHZENVMMDPQPYZJ4VX7YRORIO3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NHZENVMMDPQPYZJ4VX7YRORIO3/action/storage_attestation","attest_author":"https://pith.science/pith/NHZENVMMDPQPYZJ4VX7YRORIO3/action/author_attestation","sign_citation":"https://pith.science/pith/NHZENVMMDPQPYZJ4VX7YRORIO3/action/citation_signature","submit_replication":"https://pith.science/pith/NHZENVMMDPQPYZJ4VX7YRORIO3/action/replication_record"}},"created_at":"2026-07-05T05:56:45.090249+00:00","updated_at":"2026-07-05T05:56:45.090249+00:00"}