{"id":"W4366683629","doi":"10.1145/3592534","title":"A Method to Classify Data Quality for Decision Making Under Uncertainty","year":2023,"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":"Université Laval","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Data quality; Quality (philosophy); Context (archaeology); Process (computing); Data mining; Decision support system; Risk analysis (engineering); Data science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0236772,0.001729151,0.00196136,0.01181441,0.002362009,0.007844283,0.002809549,0.002690065,0.002918512],"category_scores_gemma":[0.07481039,0.0007125044,0.002684148,0.007943411,0.003243072,0.006849261,0.003300268,0.004078655,0.0007741687],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003898165,"about_ca_system_score_gemma":0.004429591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004378161,"about_ca_topic_score_gemma":0.003098162,"domain_scores_codex":[0.9731752,0.01010255,0.003593683,0.003178646,0.009102407,0.0008475213],"domain_scores_gemma":[0.9261028,0.04548399,0.00754509,0.005876682,0.01401466,0.0009768547],"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":[0.0004043903,0.000371042,0.01369917,0.001150085,0.0004142151,0.0003011497,0.002569301,0.05980819,0.006512091,0.1879009,0.007194345,0.7196752],"study_design_scores_gemma":[0.000145275,0.0003896799,0.005375887,0.0009143389,0.0002896304,0.0006131352,0.001368924,0.7195312,0.01037256,0.2287335,0.03195264,0.0003131651],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003335367,0.000199166,0.9932061,0.0004902125,0.00007266102,0.0003352838,0.0001735676,0.0003925505,0.001795064],"genre_scores_gemma":[0.06379846,0.000140985,0.9344884,0.0001172867,0.00006812442,0.0005521444,0.0002207736,0.0000540097,0.000559727],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0236772,"threshold_uncertainty_score":0.1252185,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6503576233917492,"score_gpt":0.6216140905973337,"score_spread":0.02874353279441544,"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."}}