{"id":"W2020076260","doi":"10.1109/thms.2014.2325558","title":"Anticipation in Driving: The Role of Experience in the Efficacy of Pre-event Conflict Cues","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Networks of Centres of Excellence of Canada","keywords":"Anticipation (artificial intelligence); Competence (human resources); Event (particle physics); Psychology; Cognitive psychology; Driving simulator; Computer science; Social psychology; Simulation; Artificial intelligence","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":[0.0009235712,0.0002036686,0.0001483881,0.0003703517,0.0002401547,0.001075681,0.000226907,0.0003982577,0.001449494],"category_scores_gemma":[0.01037615,0.0001972033,0.0001800119,0.0001472226,0.0004859065,0.0006675174,0.0008085392,0.0004750827,0.00009389163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002311857,"about_ca_system_score_gemma":0.000348898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009196522,"about_ca_topic_score_gemma":0.0009155571,"domain_scores_codex":[0.9995395,0.0001613589,0.00003019238,0.00008210562,0.0001142737,0.00007267803],"domain_scores_gemma":[0.9940786,0.003458634,0.001185878,0.0003184706,0.0003963173,0.0005621111],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.005753926,0.001615727,0.6667389,0.0006083893,0.0002783586,0.0008100277,0.01725558,0.00529363,0.137038,0.004856066,0.0005027701,0.1592486],"study_design_scores_gemma":[0.00003446651,0.001993599,0.9825435,0.00005234267,0.00007941216,0.0003882478,0.003022467,0.003250103,0.005296553,0.002240194,0.001034223,0.00006487641],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9960427,0.0001778013,0.00151214,0.00003985083,0.000007075744,0.00001234095,0.00002004975,0.000005894436,0.002182035],"genre_scores_gemma":[0.9993687,0.00005481187,0.0004355546,0.000009357142,0.00000396328,0.000005990792,0.00001645858,0.000002300064,0.0001029158],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001449494,"threshold_uncertainty_score":0.004884362,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04813239138525834,"score_gpt":0.4022283825460909,"score_spread":0.3540959911608326,"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."}}