{"id":"W2908123047","doi":"10.1101/511337","title":"Combining 3D-MOT with motor and perceptual decision-making tasks: conception of a life-sized virtual perceptual-cognitive training paradigm","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Hospitalier de l’Université de Montréal; École de Technologie Supérieure; Université de Montréal","funders":"","keywords":"Task (project management); Perception; Cognition; Computer science; Dual (grammatical number); Cognitive psychology; Training (meteorology); Psychology; Engineering; 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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008569967,0.0008107263,0.001304545,0.0006293758,0.0002789287,0.0002245295,0.0004250662,0.0008078141,0.002900255],"category_scores_gemma":[0.0006786883,0.0008194104,0.0002290937,0.0003656175,0.0005350884,0.0003479409,0.0002955135,0.001346505,0.0003001809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002156083,"about_ca_system_score_gemma":0.0006790396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004579495,"about_ca_topic_score_gemma":0.000005748543,"domain_scores_codex":[0.9953839,0.0005724318,0.00126379,0.001458099,0.000659571,0.000662271],"domain_scores_gemma":[0.9956584,0.001178398,0.001231581,0.0009469783,0.000638322,0.0003463188],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.04334716,0.007752335,0.1647504,0.004181273,0.01977909,0.001028949,0.2030206,0.004697489,0.4776464,0.06148135,0.005518825,0.006796166],"study_design_scores_gemma":[0.013649,0.002090429,0.9475844,0.008700207,0.0009112732,0.000001586264,0.01202111,0.008585247,0.0007419733,0.00001560994,0.002082264,0.003616926],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9733613,0.000294865,0.0217493,0.00006740733,0.002083477,0.001309668,0.0004238645,0.0004125866,0.0002975253],"genre_scores_gemma":[0.9953962,0.00005129366,0.003174775,0.0004550356,0.0004308101,0.0002730482,0.000002758539,0.0001890533,0.00002702873],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.782834,"threshold_uncertainty_score":0.9994256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02682369645767373,"score_gpt":0.2947374567971301,"score_spread":0.2679137603394564,"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."}}