{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002453529,0.0001308406,0.0001502086,0.0002775577,0.0002728171,0.0003523822,0.0002081818,0.0001979666,0.00224165],"category_scores_gemma":[0.0012736,0.00008949771,0.0001954936,0.0001938208,0.0001818141,0.0002726764,0.0003298752,0.0001891472,0.000198099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000237476,"about_ca_system_score_gemma":0.0001670465,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01019583,"about_ca_topic_score_gemma":0.01443116,"domain_scores_codex":[0.9998245,0.00004113555,0.00001153475,0.00003420233,0.00004656422,0.00004209359],"domain_scores_gemma":[0.9986603,0.0005642527,0.0002877018,0.00009312531,0.0002101776,0.0001844604],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005630284,0.000414664,0.9452461,0.00007679124,0.0001025462,0.0002290705,0.00355081,0.001276082,0.02358979,0.0001244854,0.0002217659,0.0246049],"study_design_scores_gemma":[0.000003109903,0.0004430439,0.993749,0.000007938977,0.00001917454,0.00008496191,0.001923477,0.0009693586,0.002343878,0.00003843796,0.000408142,0.000009459498],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9995708,0.00001094769,0.00004709868,0.000002044574,3.172682e-7,0.000003267868,0.00001855665,0.000001796295,0.0003450234],"genre_scores_gemma":[0.9994979,0.00001609671,0.00006837532,0.000001779574,4.609413e-7,0.000003696211,0.00004819658,0.000001014224,0.0003624568],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01019583,"threshold_uncertainty_score":0.02027297,"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."}}