{"id":"W4237133303","doi":"10.1177/0361198106196600117","title":"Virtual Commercial Vehicle Compliance Stations","year":2006,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Traffic and Road Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"California Department of Transportation","keywords":"Software deployment; Enforcement; Law enforcement; Transport engineering; Business; Compliance (psychology); Key (lock); Engineering; Computer security; Computer science; Political science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001593956,0.0003612037,0.0001663142,0.0008414344,0.002053632,0.00216682,0.001869558,0.0006091064,0.04805473],"category_scores_gemma":[0.005831691,0.0003598411,0.0002847657,0.001642846,0.0008580335,0.001694895,0.00204172,0.0006520299,0.004783058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001767017,"about_ca_system_score_gemma":0.003624473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01299003,"about_ca_topic_score_gemma":0.01601819,"domain_scores_codex":[0.9978086,0.0006780925,0.0001150674,0.0003830897,0.0006835725,0.0003316045],"domain_scores_gemma":[0.9933255,0.001456848,0.001016165,0.001302606,0.001779893,0.001119113],"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":[0.001003587,0.001850557,0.09842011,0.000684738,0.00007695248,0.0007652534,0.005738714,0.01292244,0.007140066,0.03990867,0.09082702,0.740662],"study_design_scores_gemma":[0.0003803307,0.003916943,0.1152553,0.0004234588,0.0001312945,0.00133665,0.02018009,0.02342949,0.01662927,0.006561478,0.8115832,0.0001724759],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6218112,0.0004098736,0.04142722,0.001772978,0.0004475072,0.001055339,0.002334315,0.003853394,0.3268882],"genre_scores_gemma":[0.9374547,0.0002099777,0.01241723,0.0001949055,0.00007013768,0.0003320533,0.001695241,0.0001042379,0.04752153],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04805473,"threshold_uncertainty_score":0.1607591,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08153970720937126,"score_gpt":0.3531969120818957,"score_spread":0.2716572048725245,"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."}}