{"id":"W3160036765","doi":"10.32473/flairs.v34i1.128474","title":"Confusion detection using cognitive ability tests","year":2021,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Confusion; Memorization; Cognition; Computer science; Support vector machine; Artificial intelligence; Cognitive psychology; Orientation (vector space); Psychology; Pattern recognition (psychology); Machine learning; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0010414,0.0008287616,0.0005184456,0.002332775,0.0001942641,0.001321779,0.0003792936,0.0006873987,0.002180744],"category_scores_gemma":[0.01103686,0.0001217904,0.0004054035,0.0008166933,0.0002674165,0.001074138,0.0006931432,0.0005322536,0.0007603847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002948244,"about_ca_system_score_gemma":0.0002819018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002012227,"about_ca_topic_score_gemma":0.001801598,"domain_scores_codex":[0.9989356,0.00020601,0.0001203258,0.0001809618,0.0004584484,0.00009861363],"domain_scores_gemma":[0.9960915,0.001361814,0.0009785483,0.0001509325,0.00112135,0.0002957887],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002551363,0.0009651523,0.5192349,0.0004490184,0.0003593029,0.0005519817,0.001287802,0.006486401,0.04667344,0.0007428661,0.002810459,0.4178873],"study_design_scores_gemma":[0.00008966295,0.00257901,0.8986235,0.00009291474,0.0001475622,0.001266106,0.001068291,0.06375525,0.02894745,0.001703231,0.001575889,0.0001511115],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.959612,0.0003399925,0.03275851,0.000126823,0.00005620109,0.0003624059,0.0009390139,0.0006462109,0.005158805],"genre_scores_gemma":[0.9920922,0.0001276542,0.006417106,0.00004158713,0.00002227421,0.00008873231,0.0004275768,0.00001436212,0.0007685322],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002332775,"threshold_uncertainty_score":0.00729537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2393936907274938,"score_gpt":0.4125526449569652,"score_spread":0.1731589542294714,"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."}}