{"id":"W2075418616","doi":"10.1518/155534309x441853","title":"Modeling SGOMS in ACT-R: Linking Macro- and Microcognition","year":2009,"lang":"en","type":"article","venue":"Journal of Cognitive Engineering and Decision Making","topic":"Cognitive Science and Mapping","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Cognitive architecture; Sociotechnical system; Computer science; Cognitive science; Cognition; Architecture; Cognitive model; Macro; Selection (genetic algorithm); Software engineering; Human–computer interaction; Artificial intelligence; Psychology; 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":[0.0008371511,0.0001392552,0.0002305478,0.000628207,0.00008424593,0.000262279,0.0001702065,0.00005629802,0.000001209878],"category_scores_gemma":[0.0004505336,0.0001270715,0.00005152858,0.0004010658,0.00001622358,0.000980793,0.00009194842,0.0003064891,0.000001045837],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002509276,"about_ca_system_score_gemma":0.00003327146,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.055186e-7,"about_ca_topic_score_gemma":8.594808e-7,"domain_scores_codex":[0.9988231,0.00002037317,0.0004098497,0.0002247536,0.0002862916,0.0002356307],"domain_scores_gemma":[0.999001,0.0004696217,0.0001217718,0.00006096265,0.0002588478,0.00008784988],"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":[0.00003758687,0.00002452611,0.0003546124,0.00001008994,0.000007386715,0.0001219183,0.0008950559,0.005492839,0.003367255,0.0005924872,0.000001943853,0.9890943],"study_design_scores_gemma":[0.001078335,0.0002129062,0.01365857,0.00472171,0.00001491999,0.000644374,0.0002621099,0.9583727,0.0003951846,0.02035169,0.00002735033,0.0002601139],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4502419,0.0006154425,0.5488778,0.00004775861,0.00008670105,0.00003927438,2.886146e-7,0.00001224034,0.00007862169],"genre_scores_gemma":[0.9482554,0.0002226056,0.05121464,0.0002169943,0.00008342957,6.945835e-7,1.925166e-7,0.000004980991,0.000001093808],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9888342,"threshold_uncertainty_score":0.5181827,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01489880722757877,"score_gpt":0.2751815534824121,"score_spread":0.2602827462548333,"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."}}