{"id":"W2557723499","doi":"10.4018/ijcini.2016100101","title":"Cognitive Intelligence","year":2016,"lang":"en","type":"article","venue":"International Journal of Cognitive Informatics and Natural Intelligence","topic":"Cognitive Computing and Networks","field":"Computer Science","cited_by":76,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina; University of New Brunswick; University of Calgary","funders":"","keywords":"Cognitive computing; Computer science; Cognition; Big data; Field (mathematics); Informatics; Cognitive science; Artificial intelligence; Data science; Theme (computing); Deep learning; Set (abstract data type); World Wide Web; Psychology; Data mining; Programming language","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":[0.0006448815,0.0002151356,0.0002534297,0.0003836079,0.00008357878,0.0002578071,0.001018231,0.00007239643,0.00003349881],"category_scores_gemma":[0.001438393,0.000137328,0.0001428502,0.0002621046,0.000243711,0.001538981,0.0004439213,0.0003546364,0.00005614475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005272334,"about_ca_system_score_gemma":0.000103165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002363371,"about_ca_topic_score_gemma":0.000001686938,"domain_scores_codex":[0.9978913,0.0000663653,0.0009391856,0.0001577317,0.0006783446,0.0002670543],"domain_scores_gemma":[0.9930041,0.002552163,0.0007421601,0.00009319239,0.003449272,0.0001591442],"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":[0.0001151118,0.00004431955,0.0004039735,0.000005103319,0.0001570749,0.00005426557,0.001220128,0.000007595329,0.00004848133,0.008899529,0.00006113556,0.9889833],"study_design_scores_gemma":[0.007282605,0.006028187,0.02812546,0.03740329,0.0004893284,0.01637246,0.01675365,0.2994857,0.2976802,0.2762552,0.008813018,0.005310889],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05425052,0.0008922327,0.941178,0.0006354208,0.001716753,0.00009751538,0.00001179229,0.0000307386,0.001187001],"genre_scores_gemma":[0.9905844,0.001665345,0.006497121,0.000864586,0.0002749757,0.000002169115,0.000001813036,0.000007239692,0.0001023057],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9836724,"threshold_uncertainty_score":0.5600075,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02024512866262602,"score_gpt":0.3028763641483745,"score_spread":0.2826312354857485,"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."}}