{"id":"W2805286447","doi":"","title":"Special Track on Semantic, Logics, Information Extraction and Artificial Intelligence.","year":2018,"lang":"en","type":"article","venue":"The Florida AI Research Society","topic":"Cognitive Computing and Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Computer science; Track (disk drive); Artificial intelligence; Information extraction; Natural language processing; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002994113,0.000107368,0.00009726267,0.0000600367,0.001028052,0.0005714096,0.0005334329,0.00008947203,0.00004375251],"category_scores_gemma":[0.0002219624,0.00007648347,0.0000750262,0.0006201514,0.0004905725,0.0005739837,0.0003518558,0.0007374078,0.0004329778],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000679837,"about_ca_system_score_gemma":0.00007641363,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000252595,"about_ca_topic_score_gemma":0.00001373305,"domain_scores_codex":[0.9982342,0.0002220641,0.0002134521,0.0002500275,0.0006502555,0.0004300474],"domain_scores_gemma":[0.9983244,0.0006513917,0.00005285896,0.0003408861,0.0005468922,0.00008355561],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004811023,0.00004063196,0.00002570824,0.000009951043,0.00001873581,0.000001671399,0.007419496,0.00006670613,0.00008163323,0.04829914,0.08185794,0.8621303],"study_design_scores_gemma":[0.0001874984,0.0009985339,0.003875534,0.00009326094,0.000008577849,0.00002597489,0.002106544,0.7822099,0.009127948,0.1136978,0.08731098,0.0003574662],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09549383,0.00004818986,0.8738302,0.01325359,0.003706082,0.0005706307,0.000003767583,0.0002125679,0.01288116],"genre_scores_gemma":[0.9843568,0.0001637654,0.001440882,0.0009334238,0.01294406,0.00001096907,0.000003545421,0.000006010978,0.0001406012],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8888629,"threshold_uncertainty_score":0.7907048,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.102912450194715,"score_gpt":0.3816093090964418,"score_spread":0.2786968589017268,"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."}}