{"id":"W2953388369","doi":"10.48550/arxiv.cs/0211042","title":"Database Repairs and Analytic Tableaux","year":2002,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Database; Computer science; Information retrieval","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.004873438,0.0003210101,0.0005941849,0.0003938577,0.0001872652,0.0005186434,0.001632837,0.0001777677,0.002370232],"category_scores_gemma":[0.003385924,0.0002616112,0.000162016,0.0005049491,0.0002150715,0.0004796784,0.005296622,0.0005407066,0.002546535],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000512532,"about_ca_system_score_gemma":0.00005791797,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004942719,"about_ca_topic_score_gemma":0.0002295389,"domain_scores_codex":[0.9954724,0.0003316299,0.0009490058,0.001569876,0.001285613,0.0003914699],"domain_scores_gemma":[0.9951002,0.0006371167,0.0004691434,0.003397829,0.000163401,0.000232334],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004018227,0.0003151095,0.2675293,0.0003205318,0.0002837945,0.0002866321,0.000865608,0.0002867242,0.00004520614,0.005355996,0.697729,0.02694191],"study_design_scores_gemma":[0.0004547004,0.00005285792,0.1285025,0.0001740344,0.0001800933,0.000009090999,0.001214409,0.005312883,0.00006429163,0.01478823,0.8484893,0.0007575323],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9422476,0.001953611,0.006516075,0.006031104,0.002104678,0.0008349429,0.001071653,0.0002836275,0.03895665],"genre_scores_gemma":[0.94525,0.001240978,0.001741925,0.002188271,0.0002969123,0.00004262664,0.0003124818,0.00003230125,0.04889448],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1507603,"threshold_uncertainty_score":0.9999836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4269583216933262,"score_gpt":0.4318917152394451,"score_spread":0.00493339354611888,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}