{"id":"W2750078980","doi":"10.1016/j.artint.2017.08.003","title":"Three-valued semantics for hybrid MKNF knowledge bases revisited","year":2017,"lang":"en","type":"article","venue":"Artificial Intelligence","topic":"Logic, Reasoning, and Knowledge","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Semantics (computer science); Computer science; Programming language; Computational semantics; Artificial intelligence; Natural language processing; Cognitive science; Operational semantics; Psychology","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":[0.00456996,0.0004184164,0.0008054022,0.003138645,0.001912352,0.007346658,0.003841489,0.001585484,0.005065378],"category_scores_gemma":[0.009465055,0.0006082304,0.001652301,0.004031176,0.003999935,0.01595202,0.00398348,0.00279377,0.0007462921],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002502811,"about_ca_system_score_gemma":0.001574295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00491188,"about_ca_topic_score_gemma":0.003970946,"domain_scores_codex":[0.9969605,0.0008572515,0.0004579047,0.0004397503,0.0009695732,0.0003150739],"domain_scores_gemma":[0.9939817,0.00268459,0.0003649892,0.001381632,0.001252751,0.0003344327],"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":[0.00005842121,0.00002202542,0.0001988426,0.00008651065,0.00002915568,0.0001298746,0.000629896,0.003793807,0.000424599,0.9762695,0.0005399561,0.01781742],"study_design_scores_gemma":[0.00001733663,0.00001232377,0.0001016701,0.00006695071,0.00003314844,0.0001243735,0.0003449873,0.02589716,0.0005679083,0.966908,0.005906163,0.00002000609],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04427191,0.0009432843,0.9312627,0.002089202,0.0001512909,0.0001080304,0.0006797689,0.0004236392,0.02007021],"genre_scores_gemma":[0.6598673,0.0005661784,0.3338455,0.0003614162,0.0001119114,0.0001709813,0.0007359333,0.00008362428,0.004257034],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007346658,"threshold_uncertainty_score":0.02416855,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1030809784977743,"score_gpt":0.3466754567955799,"score_spread":0.2435944782978056,"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."}}