{"id":"W2083947418","doi":"10.1504/ijiq.2014.068653","title":"Repairing integrity rules for improved data quality","year":2014,"lang":"en","type":"article","venue":"International Journal of Information Quality","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Data integrity; Constraint (computer-aided design); Data quality; Data mining; Data cleansing; Quality (philosophy); Set (abstract data type); Business rule; Domain (mathematical analysis); Data science; Business process; Database; Work in process","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":[0.03626662,0.001624597,0.002269616,0.007318707,0.002557081,0.007281277,0.005371027,0.002412217,0.00310756],"category_scores_gemma":[0.1935107,0.001471932,0.002872184,0.007640322,0.002724884,0.01022548,0.007057748,0.004655649,0.001185109],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002094986,"about_ca_system_score_gemma":0.008291084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008691045,"about_ca_topic_score_gemma":0.009836439,"domain_scores_codex":[0.9556271,0.01206712,0.007601112,0.005002197,0.01827526,0.001427388],"domain_scores_gemma":[0.7642909,0.08962894,0.03044688,0.08166741,0.03211454,0.001851402],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004806851,0.0006789713,0.04208006,0.002189849,0.0006953066,0.001661345,0.005999249,0.1063098,0.02878822,0.04676764,0.02040799,0.7439409],"study_design_scores_gemma":[0.0002282833,0.0005497067,0.01279154,0.001245872,0.0007986334,0.003041333,0.00356169,0.6206074,0.1215774,0.1291089,0.1060679,0.0004212465],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03428703,0.0008836609,0.9524109,0.002328763,0.0001654181,0.0007506402,0.001245759,0.005698867,0.002228979],"genre_scores_gemma":[0.09732234,0.0003899174,0.8980547,0.0004138416,0.00007213607,0.0001953603,0.002048191,0.0006703592,0.0008333019],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03626662,"threshold_uncertainty_score":0.1917985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3727443767581262,"score_gpt":0.5310786126391739,"score_spread":0.1583342358810477,"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."}}