{"id":"W2558161679","doi":"10.4230/lipics.icdt.2015.342","title":"From Causes for Database Queries to Repairs and Model-Based Diagnosis and Back","year":2015,"lang":"en","type":"article","venue":"DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)","topic":"Logic, Reasoning, and Knowledge","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Consistency (knowledge bases); Causality (physics); Database theory; Database; Information retrieval; Data mining; Database design; Artificial intelligence","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":[0.007089387,0.001075716,0.001100712,0.003798212,0.002112065,0.005435621,0.003288916,0.002615944,0.009380499],"category_scores_gemma":[0.05083539,0.001140163,0.003602274,0.002857534,0.006662125,0.01575966,0.00588665,0.005516071,0.0006767359],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004651245,"about_ca_system_score_gemma":0.004150114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005920225,"about_ca_topic_score_gemma":0.004619663,"domain_scores_codex":[0.990301,0.002791045,0.0006730857,0.001856204,0.003600938,0.00077765],"domain_scores_gemma":[0.9422949,0.04485261,0.003118098,0.005388061,0.003505608,0.0008407893],"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":[0.0000917134,0.0000644416,0.001907774,0.0002686194,0.00005899907,0.0003591629,0.0007966189,0.03917984,0.0005977137,0.9223742,0.001924094,0.03237687],"study_design_scores_gemma":[0.00001850071,0.00001882089,0.0002408428,0.00005348432,0.00005681768,0.0001794158,0.0002597482,0.070444,0.001327292,0.9241361,0.003239022,0.00002589845],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01911591,0.0008698975,0.9684148,0.004351528,0.0001620367,0.0001182249,0.0003085174,0.0005097744,0.006149352],"genre_scores_gemma":[0.624981,0.00203423,0.3599459,0.001574159,0.0005223816,0.0003966376,0.001114774,0.0003564591,0.009074464],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009380499,"threshold_uncertainty_score":0.03749275,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0445754772296752,"score_gpt":0.2825367042389375,"score_spread":0.2379612270092623,"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."}}