{"id":"W2114587893","doi":"10.1109/sccc.2005.1587860","title":"Optimizing Repair Programs for Consistent Query Answering","year":2006,"lang":"en","type":"article","venue":"","topic":"Logic, Reasoning, and Knowledge","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Query language; Query optimization; Semantics (computer science); Data integrity; Logic programming; Database; Computation; Theoretical computer science; Programming language; Deductive database; Information retrieval","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":[0.002404163,0.0005799101,0.0007870649,0.0007553934,0.0005450093,0.002019387,0.001413256,0.0008683561,0.001763089],"category_scores_gemma":[0.009166348,0.0006473837,0.0008404184,0.001096301,0.001482213,0.003252388,0.001906113,0.001574969,0.000221999],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001851603,"about_ca_system_score_gemma":0.001702099,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003821816,"about_ca_topic_score_gemma":0.005309807,"domain_scores_codex":[0.9974046,0.0007505136,0.0001708752,0.000434698,0.0009237287,0.0003155301],"domain_scores_gemma":[0.9957654,0.002906433,0.0003117096,0.0005534317,0.0003818331,0.00008110444],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0007496749,0.0005145306,0.003799075,0.0004237312,0.0001030335,0.000251231,0.0008536308,0.5353286,0.03187049,0.1937257,0.004308761,0.2280715],"study_design_scores_gemma":[0.00007320677,0.0001029664,0.0002869484,0.00001681889,0.00004812289,0.00006267975,0.000184788,0.8970022,0.01409131,0.0861476,0.001964213,0.00001910297],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1580486,0.0002224229,0.8351686,0.0004601344,0.00001607536,0.0001404429,0.0001600971,0.002820431,0.002963236],"genre_scores_gemma":[0.5875628,0.0001901181,0.4088194,0.0001669293,0.00001817795,0.0002579186,0.0004685014,0.0005591228,0.00195701],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003821816,"threshold_uncertainty_score":0.01343441,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0226620025609064,"score_gpt":0.2386941877532323,"score_spread":0.2160321851923259,"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."}}