{"id":"W1509029601","doi":"10.1002/atr.182","title":"Heavy commercial vehicles‐following behavior and interactions with different vehicle classes","year":2011,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic control and management","field":"Engineering","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Truck; Transport engineering; Heavy traffic; Heavy load; Traffic flow (computer networking); Automotive engineering; Work (physics); Heavy equipment; Commercial vehicle; Engineering; Computer science; Computer security","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00002875934,0.00008469449,0.0001389935,0.00006859247,0.00003422083,0.000009986921,0.00003840248,0.00001630398,0.000009134458],"category_scores_gemma":[0.000001070164,0.00006759337,0.00005304922,0.00004862872,0.00001074669,0.0003319673,8.136143e-7,0.0001223712,3.690946e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000228873,"about_ca_system_score_gemma":0.000004612368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004456348,"about_ca_topic_score_gemma":0.0003227901,"domain_scores_codex":[0.9995215,0.00000573294,0.0002268177,0.00005314787,0.0001038385,0.00008896334],"domain_scores_gemma":[0.9997926,0.00001529025,0.00006368782,0.00004405617,0.00003121825,0.00005318931],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001798312,0.0009530784,0.1311687,0.0002520635,0.0008510762,0.0005890734,0.01807057,0.1525353,0.05395823,0.0008924437,0.000154988,0.6387761],"study_design_scores_gemma":[0.001154436,0.0001701159,0.9961693,0.00006188531,0.0001819436,0.000005999594,0.0005547622,0.00007733705,0.0009857335,0.00002767038,0.000526029,0.00008482241],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9959706,0.000135261,0.003293483,0.00003512543,0.0003482897,0.0000901231,0.000002463717,0.00003869184,0.00008592618],"genre_scores_gemma":[0.998282,0.00007183212,0.00156225,0.00001392046,0.00003707893,0.000008160228,0.00000297232,0.00001318673,0.000008603615],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8650005,"threshold_uncertainty_score":0.2756377,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0118519807009644,"score_gpt":0.2255540711606804,"score_spread":0.2137020904597159,"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."}}