{"id":"W584401833","doi":"","title":"A Methodology for Container Truck Traffic Data Collection for Inland Port Cities","year":2010,"lang":"en","type":"article","venue":"Transportation Research Board 89th Annual MeetingTransportation Research Board","topic":"Urban and Freight Transport Logistics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Truck; Container (type theory); Transport engineering; Data collection; Port (circuit theory); Traffic flow (computer networking); Engineering; Computer science; Automotive engineering; Computer security","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","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0102738,0.0005900452,0.0008903571,0.001544632,0.001121644,0.000203952,0.00134347,0.0007608226,0.0002309036],"category_scores_gemma":[0.001096684,0.0006133547,0.000307865,0.00170366,0.00111459,0.0008302641,0.00001279029,0.002312273,0.00002642496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001428643,"about_ca_system_score_gemma":0.0006728293,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001638378,"about_ca_topic_score_gemma":0.02776554,"domain_scores_codex":[0.9922681,0.000464667,0.001593506,0.001394237,0.002075304,0.002204167],"domain_scores_gemma":[0.9896592,0.004440465,0.0001498016,0.001174647,0.003855515,0.0007203087],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0185971,0.001866788,0.1224272,0.01218871,0.002534204,0.0004199905,0.04259381,0.03872762,0.05734421,0.1021466,0.5607004,0.0404533],"study_design_scores_gemma":[0.0133082,0.003018942,0.2295506,0.0002931024,0.0004925854,0.000006546843,0.01438025,0.06208701,0.007842341,0.01022336,0.6561906,0.002606453],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8687594,0.0004258293,0.1091115,0.001266956,0.001814883,0.007452997,0.008840435,0.001063209,0.001264851],"genre_scores_gemma":[0.9230422,0.0004127875,0.0615976,0.000065197,0.000813841,0.002613828,0.009007201,0.0002836518,0.00216372],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1071234,"threshold_uncertainty_score":0.9999894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2131241826413824,"score_gpt":0.4012523689019382,"score_spread":0.1881281862605559,"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."}}