{"id":"W2554048096","doi":"10.1016/j.ijar.2017.07.010","title":"Causes for query answers from databases: Datalog abduction, view-updates, and integrity constraints","year":2017,"lang":"en","type":"preprint","venue":"International Journal of Approximate Reasoning","topic":"Logic, Reasoning, and Knowledge","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Causality (physics); Datalog; Data integrity; Constraint (computer-aided design); Computer science; Consistency (knowledge bases); Connection (principal bundle); Theoretical computer science; Database; Data mining; Mathematics; 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":[0.01497651,0.001168748,0.001875823,0.004680156,0.002042843,0.008785497,0.003608808,0.003534909,0.003860034],"category_scores_gemma":[0.1123326,0.001973539,0.003567982,0.00392743,0.003732248,0.01419667,0.006717499,0.006359194,0.0003804958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00294446,"about_ca_system_score_gemma":0.004439241,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007914451,"about_ca_topic_score_gemma":0.009560614,"domain_scores_codex":[0.9812654,0.005557865,0.002175011,0.002174247,0.007549399,0.001278108],"domain_scores_gemma":[0.8995548,0.07281447,0.006682686,0.01140326,0.008450596,0.001094111],"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":[0.0009913911,0.0002736298,0.01998113,0.001085592,0.0006465486,0.002208564,0.002950602,0.08506434,0.002165697,0.7660049,0.01132369,0.1073039],"study_design_scores_gemma":[0.0001067953,0.00002966969,0.0008776074,0.0001380211,0.0002801821,0.0004540471,0.0006329675,0.2340016,0.003681236,0.7570254,0.002720192,0.00005227303],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08270874,0.002273931,0.894403,0.01078888,0.0003046896,0.0002622038,0.001524407,0.001512453,0.006221752],"genre_scores_gemma":[0.7741275,0.001470257,0.2164234,0.001347769,0.0005924178,0.0002590996,0.001714633,0.0004416222,0.003623184],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01497651,"threshold_uncertainty_score":0.07920432,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05219284208451504,"score_gpt":0.3314246874115469,"score_spread":0.2792318453270319,"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."}}