{"id":"W2311043595","doi":"","title":"A consistency-based system for knowledge base merging","year":2006,"lang":"en","type":"dissertation","venue":"Summit (Simon Fraser University)","topic":"Logic, Reasoning, and Knowledge","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Simon Fraser University","keywords":"Knowledge base; Consistency (knowledge bases); Merge (version control); Belief revision; Computer science; Vocabulary; Propositional calculus; Problem solver; Base (topology); Theoretical computer science; Weak consistency; Knowledge-based systems; Process (computing); Solver; Artificial intelligence; Algorithm; Mathematics; Information retrieval; Strong consistency; Programming language; Software engineering; Linguistics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002889116,0.0005582843,0.0006485991,0.001002648,0.0006219772,0.0001865381,0.001694934,0.000514781,0.00001798922],"category_scores_gemma":[0.00007558169,0.0006186148,0.0005474312,0.001207934,0.00006533991,0.000396357,0.0001129553,0.0003333542,0.0001265133],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005955492,"about_ca_system_score_gemma":0.001162412,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000211266,"about_ca_topic_score_gemma":0.05760685,"domain_scores_codex":[0.9974062,0.0001658173,0.0003550874,0.00108418,0.0002919009,0.0006968486],"domain_scores_gemma":[0.9974298,0.0003816099,0.000405326,0.0008778232,0.0006452444,0.0002601579],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000848008,0.00161221,0.03021773,0.009356111,0.0007808214,0.001844016,0.0008630578,0.0007354249,0.0001173436,0.6480229,0.2797759,0.02582644],"study_design_scores_gemma":[0.006791318,0.0004439014,0.0004154772,0.001995832,0.0008756757,7.286314e-8,0.02797028,0.05815529,0.01205807,0.0006215094,0.8875877,0.003084924],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.03106935,0.002984959,0.2418031,0.0001225909,0.008102709,0.002479245,0.0002453271,0.002314535,0.7108781],"genre_scores_gemma":[0.8637243,0.00002256638,0.009000585,0.00006004093,0.0004263508,0.00002755841,0.001714961,0.0001139601,0.1249097],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8326549,"threshold_uncertainty_score":0.9996265,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01421867465797648,"score_gpt":0.2194869045890782,"score_spread":0.2052682299311018,"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."}}