{"id":"W4399448652","doi":"","title":"Affective AIED: From Detection to Adaptation","year":2009,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Adaptation (eye); Computer science; Artificial intelligence; Cognitive psychology; Computer vision; Speech recognition; Psychology; Neuroscience","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":[0.002170388,0.0001868217,0.0001737804,0.0002018737,0.0003937288,0.000450765,0.00115867,0.00009737036,0.00004002422],"category_scores_gemma":[0.001279986,0.0002073697,0.00008852004,0.0009957849,0.00005038421,0.0007192597,0.000222765,0.0001978323,0.0003391462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001517024,"about_ca_system_score_gemma":0.00008262847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002343674,"about_ca_topic_score_gemma":0.004572149,"domain_scores_codex":[0.9963752,0.001942039,0.0003128753,0.000655478,0.0003746466,0.0003397286],"domain_scores_gemma":[0.9960982,0.0009591333,0.0001670266,0.00135602,0.001215265,0.0002043841],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001219979,0.0002664858,0.0001066913,0.000003268784,0.00001382713,0.000005175611,0.02064636,0.0004758512,0.05288452,0.178593,0.0002186227,0.746774],"study_design_scores_gemma":[0.0001732233,0.00000316476,0.006735518,0.0001577953,0.000008328803,0.000004320839,0.0003409741,0.1584146,0.7957355,0.03403845,0.004066432,0.0003216615],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1027757,0.00009167688,0.8724943,0.008909667,0.0001611742,0.0002882188,0.000003934001,0.0003506759,0.0149247],"genre_scores_gemma":[0.8873082,0.00002094218,0.1112443,0.0004386762,0.0000234286,0.00002637315,0.00001512919,0.00001150774,0.0009114638],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7845325,"threshold_uncertainty_score":0.845629,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01515921205131539,"score_gpt":0.2310052695358141,"score_spread":0.2158460574844987,"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."}}