{"id":"W2386079104","doi":"","title":"Modeling Safe Motion Parameters of Transportation Modes Using Sensitivity Learning Method","year":2015,"lang":"en","type":"article","venue":"Road Traffic & Safety","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Transport Canada","funders":"","keywords":"Sensitivity (control systems); Acceleration; Motion (physics); Markov chain; Turning radius; Computer science; Adaptation (eye); Engineering; Simulation; Control theory (sociology); Artificial intelligence; Physics; Mechanical engineering; Machine learning","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":[0.00160201,0.00107935,0.0007351863,0.001300796,0.0003676854,0.0008622548,0.0007383297,0.0008321187,0.001150359],"category_scores_gemma":[0.00452369,0.0005816577,0.001403454,0.0006400397,0.000690837,0.001348627,0.0007975837,0.001064346,0.000127656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001030466,"about_ca_system_score_gemma":0.001121657,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.013341,"about_ca_topic_score_gemma":0.005022848,"domain_scores_codex":[0.9991965,0.0002567667,0.00005016183,0.0002374233,0.0001468625,0.0001123156],"domain_scores_gemma":[0.9978982,0.001488356,0.0002302346,0.00008606554,0.0002470702,0.00005011242],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001350645,0.00001651244,0.001295838,0.00001404373,0.00002856257,0.0000237654,0.00002240517,0.9909753,0.000590845,0.002012856,0.00005663404,0.004949648],"study_design_scores_gemma":[7.126796e-7,0.000005481269,0.0001733554,0.000001455931,0.000003379695,0.000003403969,0.000002335931,0.9987363,0.0001265411,0.0009208526,0.00002297752,0.00000313186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07452056,0.0001094484,0.9237435,0.0001057835,0.00001683192,0.00005071137,0.00007237445,0.0001639656,0.001216881],"genre_scores_gemma":[0.9730942,0.0001290695,0.02540899,0.0000297993,0.00001370446,0.0000909224,0.0001130196,0.00001869821,0.001101528],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.013341,"threshold_uncertainty_score":0.02652669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0326557701469021,"score_gpt":0.2614498865842507,"score_spread":0.2287941164373486,"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."}}