{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007198064,0.001284347,0.0006648634,0.009521568,0.001925822,0.003149284,0.001872787,0.0005906718,0.007314641],"category_scores_gemma":[0.01253505,0.001114029,0.001256522,0.008849166,0.000497783,0.001942334,0.001966083,0.0014164,0.00411951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002055319,"about_ca_system_score_gemma":0.008863845,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03178967,"about_ca_topic_score_gemma":0.03583007,"domain_scores_codex":[0.992229,0.00157311,0.001404795,0.001297772,0.003190374,0.0003049138],"domain_scores_gemma":[0.9862906,0.001807468,0.001129024,0.002137511,0.008279653,0.0003557306],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001994646,0.001078134,0.05087586,0.001186871,0.0001961508,0.0008871123,0.004235263,0.02017997,0.04582963,0.02343413,0.05806039,0.7938371],"study_design_scores_gemma":[0.0002554953,0.0007727742,0.0926544,0.000911298,0.0001325775,0.001339332,0.006809389,0.1409556,0.1015491,0.007900517,0.6461721,0.0005473376],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01150819,0.00007021277,0.9479699,0.0002254798,0.0001000698,0.009347902,0.01594191,0.00655397,0.008282462],"genre_scores_gemma":[0.01512042,0.00009097143,0.960315,0.00005170884,0.0000158959,0.007866002,0.01316037,0.0003590024,0.003020612],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03178967,"threshold_uncertainty_score":0.06320924,"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."}}