{"id":"W2035931420","doi":"10.4018/jssci.2010100103","title":"Perspectives on Cognitive Computing and Applications","year":2010,"lang":"en","type":"article","venue":"International Journal of Software Science and Computational Intelligence","topic":"Cognitive Computing and Networks","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina; University of Alberta; University of Calgary","funders":"","keywords":"Cognitive computing; Computer science; Cognition; Informatics; Cognitive architecture; Field (mathematics); Data science; Von Neumann architecture; Cognitive science; LIDA; Artificial intelligence; Human–computer interaction; Psychology","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":[0.0009899033,0.0001249762,0.0001295698,0.0004302385,0.000307161,0.0004617172,0.000952536,0.00003694729,0.00000646176],"category_scores_gemma":[0.0008390189,0.0001115723,0.00004184433,0.0004825067,0.0007644745,0.0006267235,0.0003285647,0.0004076083,0.00000740699],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000388124,"about_ca_system_score_gemma":0.0003055775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003296984,"about_ca_topic_score_gemma":0.000001166412,"domain_scores_codex":[0.9981607,0.00002651782,0.0003288525,0.0003626758,0.0009479753,0.0001732627],"domain_scores_gemma":[0.9944156,0.00112603,0.0002823608,0.00009109882,0.003922066,0.0001628624],"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":[0.00001848042,0.00009727763,0.001162155,0.000003071124,0.0000287211,0.00001733282,0.001410912,0.002079387,0.00009463346,0.1510922,0.00002175902,0.8439741],"study_design_scores_gemma":[0.001306259,0.001279214,0.09025957,0.0009297532,0.0000474557,0.00492844,0.006428299,0.5137941,0.00366951,0.3737933,0.002313029,0.001251108],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08205815,0.0002133767,0.9161257,0.0005873111,0.0005643247,0.00008317216,0.000003102948,0.00003360412,0.0003312254],"genre_scores_gemma":[0.9364693,0.00007708975,0.06262768,0.0004805331,0.0003310159,0.000001931719,7.316538e-7,0.000004272829,0.000007505148],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8544111,"threshold_uncertainty_score":0.4549788,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01483821310925973,"score_gpt":0.318869128687606,"score_spread":0.3040309155783462,"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."}}