{"id":"W623566820","doi":"","title":"Using GPS and GIS Technologies to Analyze Truck Drivers' Compliance with Traffic Regulations","year":2007,"lang":"en","type":"article","venue":"Transportation Research Board 86th Annual MeetingTransportation Research Board","topic":"Safety Warnings and Signage","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Truck; Transport engineering; Global Positioning System; Pedestrian; Computer science; Engineering; Automotive engineering; Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.006556813,0.0005109595,0.0006525989,0.002786621,0.001571183,0.0001892329,0.000762892,0.0004959903,0.0004515398],"category_scores_gemma":[0.0003009514,0.0004948088,0.0001568056,0.005180822,0.001538964,0.0006015302,0.00002420911,0.00199076,0.000129087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002409892,"about_ca_system_score_gemma":0.0002933909,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00382998,"about_ca_topic_score_gemma":0.01859291,"domain_scores_codex":[0.9909229,0.0006480138,0.001206352,0.001626125,0.003206936,0.0023897],"domain_scores_gemma":[0.9937564,0.001351305,0.000243773,0.0008402527,0.003006438,0.0008018815],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01468544,0.001664114,0.590683,0.001109636,0.001144351,0.002987107,0.1839679,0.02149871,0.02270897,0.05030559,0.01059086,0.0986544],"study_design_scores_gemma":[0.00218129,0.00117849,0.8897033,0.0003784326,0.00006825056,0.000004905455,0.09411815,0.0003572688,0.0007765344,0.0006131448,0.009947203,0.0006730418],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9807687,0.0002912548,0.01027577,0.002982357,0.0001467803,0.002260469,0.000275318,0.0005696384,0.002429729],"genre_scores_gemma":[0.9837415,0.00007301992,0.01409997,0.00008455689,0.000115235,0.0002275446,0.0001814757,0.0001210727,0.001355612],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2990203,"threshold_uncertainty_score":0.9997504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1035143748442116,"score_gpt":0.4242187860543142,"score_spread":0.3207044112101026,"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."}}