{"id":"W2949348411","doi":"10.48550/arxiv.1008.4627","title":"Matching Dependencies with Arbitrary Attribute Values: Semantics, Query Answering and Integrity Constraints","year":2010,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; University of Ottawa","funders":"","keywords":"Tuple; Computer science; Matching (statistics); Functional dependency; Data integrity; Semantics (computer science); Data mining; Generalization; Invariant (physics); Theoretical computer science; Relational database; Database; Mathematics; Programming language; Discrete mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01171917,0.0006876316,0.001442432,0.003467264,0.001766663,0.005427374,0.003106844,0.002354183,0.001912203],"category_scores_gemma":[0.04449403,0.0009736904,0.001539706,0.007220902,0.007783853,0.01888421,0.004058829,0.003806743,0.0003164743],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003081577,"about_ca_system_score_gemma":0.002269088,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004447106,"about_ca_topic_score_gemma":0.00223531,"domain_scores_codex":[0.9869387,0.004577617,0.001462294,0.001883471,0.00439328,0.0007446774],"domain_scores_gemma":[0.9577174,0.0262966,0.00519497,0.006633325,0.00331029,0.0008473793],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00008593267,0.00008019589,0.001405342,0.0002120193,0.00004244476,0.0002365127,0.000901511,0.03300101,0.001293517,0.9321626,0.001650488,0.02892843],"study_design_scores_gemma":[0.00001946966,0.00002176053,0.0003657287,0.0000350907,0.00002145555,0.0001856124,0.0002188881,0.1121995,0.001756022,0.881457,0.003692943,0.00002652116],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05932071,0.00101948,0.9303867,0.003895699,0.00005797752,0.000140792,0.0006724728,0.0002909972,0.004215188],"genre_scores_gemma":[0.6085809,0.001351829,0.384491,0.0006149919,0.0002980187,0.0003320945,0.001175236,0.0001710813,0.002984821],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01171917,"threshold_uncertainty_score":0.06197768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2250640965752461,"score_gpt":0.2770791278655393,"score_spread":0.0520150312902932,"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."}}