{"id":"W2969192420","doi":"10.1007/978-3-030-27272-2_36","title":"An End-to-End Deep Learning Based Gesture Recognizer for Vehicle Self Parking System","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Software deployment; Automotive industry; End-to-end principle; Gesture; Robustness (evolution); Gesture recognition; Artificial intelligence; Deep learning; Classifier (UML); Computer vision; Real-time computing; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002551883,0.0008127065,0.0009749235,0.001182825,0.0005326099,0.001283188,0.003515632,0.0006609202,0.00001913904],"category_scores_gemma":[0.0001378299,0.0007463013,0.0002571803,0.0008835533,0.0001586078,0.0007910205,0.0005604043,0.001130685,0.0001993028],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006021948,"about_ca_system_score_gemma":0.0008201505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001796497,"about_ca_topic_score_gemma":0.00008840094,"domain_scores_codex":[0.9938372,0.0001942156,0.00083043,0.002636301,0.001388788,0.001113048],"domain_scores_gemma":[0.9951415,0.001416985,0.0005351568,0.001720992,0.0007298252,0.0004555271],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002435984,0.0000550312,0.0002907897,0.0004760669,0.00003976795,0.00007735809,0.002319121,0.1105197,0.0005430138,0.002590536,0.00002221792,0.883042],"study_design_scores_gemma":[0.0007409736,0.0006176096,0.0001907591,0.001507067,0.00002852175,0.0001011466,0.000002722443,0.9758192,0.001401613,0.001608886,0.01676018,0.001221356],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0002434115,0.0002700277,0.9910613,0.000453279,0.003676716,0.001667499,0.00001377151,0.0007213604,0.001892638],"genre_scores_gemma":[0.6001123,0.000005585638,0.3966137,0.001596138,0.001209674,0.00007849289,0.0000262917,0.0001014773,0.0002563705],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8818207,"threshold_uncertainty_score":0.9997536,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0173795428238001,"score_gpt":0.2445664748726611,"score_spread":0.227186932048861,"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."}}