{"id":"W3004367836","doi":"10.1142/s0218001420500330","title":"Towards Wide Range Tracking of Head Scanning Movement in Driving","year":2020,"lang":"en","type":"article","venue":"International Journal of Pattern Recognition and Artificial Intelligence","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Eye Institute; National Institutes of Health","keywords":"Orb (optics); Artificial intelligence; Computer vision; Computer science; Tracking (education); Head (geology); Simultaneous localization and mapping; Deep learning; Feature (linguistics); Metric (unit); Mobile robot; Engineering; Image (mathematics); Robot","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":[0.0007861117,0.00009673934,0.0002044517,0.000208071,0.00002398014,0.0001268978,0.0004521732,0.00003548967,0.00004242917],"category_scores_gemma":[0.0003868861,0.00009300098,0.00008441316,0.0001891033,0.00003984201,0.0005329718,0.00008900026,0.0001964225,0.000006975613],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002944074,"about_ca_system_score_gemma":0.00004620826,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006549655,"about_ca_topic_score_gemma":0.00007293843,"domain_scores_codex":[0.9984397,0.0001172832,0.0007420649,0.0001684028,0.0004069953,0.0001255429],"domain_scores_gemma":[0.9988477,0.00019666,0.0004053724,0.00005982188,0.0004072152,0.00008318404],"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":[0.00003102026,0.00005849971,0.03532819,0.00001257666,0.00002782779,0.00008137657,0.002055989,0.0002722755,0.003140404,0.0002293253,0.000004821846,0.9587577],"study_design_scores_gemma":[0.0009435886,0.00106954,0.2157876,0.003026923,0.00003411891,0.0002019759,0.002523889,0.1263253,0.504898,0.1439533,0.0003472384,0.0008885041],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3890693,0.00007067209,0.6074215,0.002941997,0.0003858012,0.00003516081,0.000003270172,0.000007442868,0.00006482359],"genre_scores_gemma":[0.9868383,0.0001524649,0.01148113,0.001320907,0.0001982201,0.000001307869,0.000001323762,0.000005508657,7.748352e-7],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9578692,"threshold_uncertainty_score":0.379247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1621286119657822,"score_gpt":0.3625894182911271,"score_spread":0.2004608063253449,"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."}}