{"id":"W2039487249","doi":"10.1109/tla.2014.6827882","title":"Using a NIR Camera for Car Gesture Control","year":2014,"lang":"en","type":"article","venue":"IEEE Latin America Transactions","topic":"Advanced Optical Sensing Technologies","field":"Physics and Astronomy","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Gesture; Gesture recognition; Computer science; Robustness (evolution); Duty cycle; Computer vision; Ranging; Artificial intelligence; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001385317,0.0003347407,0.000209128,0.0002290503,0.0001848707,0.0004225016,0.0004231201,0.0003710604,0.001853779],"category_scores_gemma":[0.0002998292,0.000146858,0.0001531348,0.0001601138,0.0002294273,0.0005483747,0.0004099465,0.0002836542,0.000500286],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002443274,"about_ca_system_score_gemma":0.0003018553,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008332347,"about_ca_topic_score_gemma":0.001618485,"domain_scores_codex":[0.9998176,0.0000186422,0.000006037708,0.00004073742,0.00009853742,0.00001842294],"domain_scores_gemma":[0.9999118,0.00001855744,0.00001240906,0.00001835011,0.00002854426,0.00001045119],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00019822,0.00004280444,0.0008017222,0.0001661289,0.00001058578,0.0002200143,0.0001123143,0.002217767,0.8138155,0.003018411,0.001065829,0.1783307],"study_design_scores_gemma":[0.00003955863,0.0006343648,0.008882568,0.00007193511,0.00007342585,0.002492097,0.000163136,0.08587249,0.851534,0.001439493,0.04869259,0.0001043255],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2194596,0.002943987,0.7268318,0.0003899058,0.0003417357,0.0002461331,0.0001736693,0.002441985,0.04717118],"genre_scores_gemma":[0.7279747,0.001327499,0.2510419,0.000263506,0.00010495,0.0001017668,0.00009865325,0.00006594865,0.01902114],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001853779,"threshold_uncertainty_score":0.006201565,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01907979023073255,"score_gpt":0.2741451405232748,"score_spread":0.2550653502925423,"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."}}