{"id":"W3126264610","doi":"10.1109/cavs51000.2020.9334636","title":"A Probabilistic Model for Visual Driver Gaze Approximation from Head Pose Estimation","year":2020,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Gaze; Computer science; Probabilistic logic; Computer vision; Artificial intelligence; Advanced driver assistance systems; Process (computing); Gaussian process; Head (geology); Kriging; Situation awareness; Interval (graph theory); Eye tracking; Visual search; Visual angle; Human–computer interaction; Gaussian; Machine learning; Engineering; Mathematics","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":[0.00005667837,0.0001176586,0.0001277223,0.00002656337,0.0000779972,0.0002139025,0.0003737645,0.00005080015,0.00001867205],"category_scores_gemma":[0.0001030692,0.0001005731,0.00004342681,0.0001913919,0.00001942115,0.0007109187,0.00009123133,0.00005628102,0.00004891873],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002306787,"about_ca_system_score_gemma":0.0001061694,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001235541,"about_ca_topic_score_gemma":0.000006269964,"domain_scores_codex":[0.9990495,0.00001243848,0.0002060675,0.0003888902,0.000170801,0.0001722443],"domain_scores_gemma":[0.9994897,0.00005561372,0.00007474356,0.0001705849,0.00009980759,0.0001095388],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006356188,0.0002436462,0.0001069985,0.0003399997,0.00002689159,0.000002896074,0.006033125,0.06699368,0.002385028,0.7679142,0.002578649,0.1533113],"study_design_scores_gemma":[0.0002670444,0.00008465898,0.0001430729,0.00001441977,0.00000614595,5.970703e-7,0.000007899289,0.8611578,0.0005104732,0.137661,0.00002435243,0.0001225779],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006258688,0.00001076506,0.9888593,0.003796808,0.00005029721,0.000384801,0.000008486883,0.0002261864,0.0004046882],"genre_scores_gemma":[0.5789809,6.544541e-7,0.4202632,0.0006224369,0.00003365076,0.00003898506,0.00001553834,0.00000471042,0.00003992571],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7941641,"threshold_uncertainty_score":0.410125,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03553313601755526,"score_gpt":0.2803356295545801,"score_spread":0.2448024935370249,"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."}}