{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00391863,0.002052666,0.003491909,0.005405185,0.002204786,0.0105303,0.002937599,0.004218956,0.330705],"category_scores_gemma":[0.006149468,0.0006456616,0.001627373,0.007129427,0.001075281,0.009647802,0.004547897,0.005149385,0.2095815],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002840464,"about_ca_system_score_gemma":0.004525859,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002489561,"about_ca_topic_score_gemma":0.008182386,"domain_scores_codex":[0.9983588,0.0001882808,0.0001068023,0.0004399614,0.0006216672,0.0002846328],"domain_scores_gemma":[0.990881,0.001347031,0.0003252346,0.0007215892,0.003057956,0.003667284],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005088006,0.00005383982,0.0001267584,0.0002954472,0.00001253686,0.00003920223,0.00002336795,0.00006381155,0.0002910475,0.002378008,0.9415129,0.05515224],"study_design_scores_gemma":[0.00001900562,0.00008283916,0.0004125692,0.0002224059,0.00001314345,0.0001008197,0.00004555874,0.0002558265,0.0002016855,0.002984142,0.9956489,0.00001308405],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"other","genre_scores_codex":[0.002034035,0.06810062,0.01360879,0.05875139,0.4270188,0.0008393084,0.00688782,0.002504227,0.4202549],"genre_scores_gemma":[0.004100721,0.03393563,0.003451408,0.004584537,0.1270902,0.0003401227,0.007157932,0.0008185135,0.818521],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.330705,"threshold_uncertainty_score":0.954668,"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."}}