{"id":"W2088206769","doi":"10.1016/j.psychsport.2014.08.001","title":"Advancing theory and application of cognitive research in sport: Using representative tasks to explain and predict skilled anticipation, decision-making, and option-generation behavior","year":2014,"lang":"en","type":"article","venue":"Psychology of sport and exercise","topic":"Sport Psychology and Performance","field":"Psychology","cited_by":60,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"","keywords":"Psychology; Cognition; Anticipation (artificial intelligence); Cognitive psychology; Perception; Applied psychology; Test (biology); Intervention (counseling); Artificial intelligence; Computer science","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":[0.002670426,0.0001599413,0.000429175,0.0005631417,0.0001379482,0.000007970132,0.00007899022,0.0002350608,0.00005315475],"category_scores_gemma":[0.00005166106,0.0001611015,0.00001925573,0.0003260929,0.0006104413,0.0001584643,0.00005660353,0.0002251944,0.000002397579],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008559045,"about_ca_system_score_gemma":0.00001214097,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001072187,"about_ca_topic_score_gemma":0.00006636498,"domain_scores_codex":[0.9983078,0.00005652704,0.0005610684,0.0006503061,0.0001502174,0.0002740974],"domain_scores_gemma":[0.999007,0.0002015984,0.0002423536,0.000279265,0.0001685379,0.0001012665],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002274427,0.000218835,0.8906337,0.00002618337,0.00001641269,0.000009631075,0.007499616,0.000009273404,0.0008563381,0.001611162,0.00005385696,0.09679057],"study_design_scores_gemma":[0.001431821,0.0003006035,0.9925274,0.0002918792,0.00006084074,0.00006150119,0.002237535,0.0003012253,0.0002147314,0.002377196,0.00005188658,0.000143444],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.993927,0.000873852,0.00372731,0.00005054317,0.0001862836,0.0008377889,0.000009870579,0.00001106099,0.0003763144],"genre_scores_gemma":[0.9972297,0.0004812596,0.00190009,0.00008085911,0.00006029351,0.0001751055,0.00003210772,0.00001731097,0.00002328595],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1018936,"threshold_uncertainty_score":0.656953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03767307252490412,"score_gpt":0.4431863676861101,"score_spread":0.405513295161206,"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."}}