{"id":"W2171069666","doi":"10.4271/2014-01-2381","title":"Heavy-Duty Vehicle Rear-View Camera Systems","year":2014,"lang":"en","type":"article","venue":"SAE International journal of commercial vehicles","topic":"Advanced Measurement and Detection Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Heavy duty; Automotive engineering; Computer science; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0004861256,0.0006359842,0.0004930421,0.001019786,0.0005008216,0.001097704,0.00123948,0.0007642086,0.0712359],"category_scores_gemma":[0.0006741102,0.0004098868,0.0003126746,0.0006109497,0.0001461077,0.001009623,0.00105149,0.0006071204,0.02759188],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006211409,"about_ca_system_score_gemma":0.0007936243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006613958,"about_ca_topic_score_gemma":0.0103156,"domain_scores_codex":[0.9991069,0.00006107941,0.00003570608,0.0002239474,0.0004927946,0.00007942293],"domain_scores_gemma":[0.9989586,0.00005730619,0.00004362087,0.0001720581,0.0007082429,0.0000602636],"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":[0.0006760096,0.0002476414,0.007064994,0.0004857297,0.00005909904,0.0002267636,0.0004101712,0.002830937,0.1775751,0.003220758,0.09589954,0.7113033],"study_design_scores_gemma":[0.0002919381,0.001318941,0.05209944,0.0002435121,0.0001503047,0.001936651,0.0005161598,0.1060228,0.2495191,0.001210944,0.5864318,0.0002583505],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1211942,0.00170792,0.5587992,0.00066245,0.0008165373,0.002008613,0.01061383,0.02750721,0.27669],"genre_scores_gemma":[0.4756536,0.001273008,0.1681439,0.0007340112,0.0002586768,0.0006984187,0.01224488,0.0009363949,0.340057],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0712359,"threshold_uncertainty_score":0.2383078,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02568721257818557,"score_gpt":0.2907759099878268,"score_spread":0.2650886974096412,"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."}}