{"id":"W2516718098","doi":"10.1145/2883616","title":"Unifying Data and Constraint Repairs","year":2016,"lang":"en","type":"article","venue":"Journal of Data and Information Quality","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Data integrity; Data quality; Constraint (computer-aided design); Scalability; Set (abstract data type); Data mining; Semantics (computer science); Data type; Data modeling; Quality (philosophy); Database; Programming language","routes":{"ca_aff":true,"ca_fund":true,"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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.02414628,0.00007083991,0.0002175969,0.0001666895,0.000105854,0.0004412346,0.00143942,0.00003512104,0.0001236983],"category_scores_gemma":[0.008258068,0.00003786568,0.00001809637,0.0001387017,0.0001789701,0.02498526,0.001700755,0.0000828897,0.00003169769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001193981,"about_ca_system_score_gemma":0.00007275897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001837763,"about_ca_topic_score_gemma":0.00001284292,"domain_scores_codex":[0.9970779,0.0002742521,0.001514367,0.0001594,0.0008687762,0.0001052387],"domain_scores_gemma":[0.9958989,0.001046205,0.001195812,0.001472221,0.0002576878,0.0001291492],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005194274,0.00001664554,0.002216785,0.00002745955,0.00003063586,0.000001500853,0.0005547655,2.561141e-7,0.00002182019,0.02468116,0.08186843,0.8905286],"study_design_scores_gemma":[0.0006963685,0.00004564319,0.01663969,0.00004771412,0.00001677873,0.00004800878,0.005993658,0.0005139337,0.00001027753,0.007119594,0.968779,0.00008927982],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1929626,0.0004199809,0.7375439,0.04379009,0.0009807753,0.0003518716,0.01283271,0.00004186376,0.01107627],"genre_scores_gemma":[0.9825918,0.001437984,0.012566,0.002776641,0.0001310992,4.021833e-7,0.0003476566,0.000003136897,0.0001452829],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8904393,"threshold_uncertainty_score":0.9886518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4241185632103378,"score_gpt":0.4856853147821305,"score_spread":0.0615667515717927,"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."}}