{"id":"W2605487561","doi":"","title":"Big Data Analysis to Measure Delays of Canadian Domestic and Cross-Border Truck Trips","year":2017,"lang":"en","type":"article","venue":"Transportation Research Board 96th Annual MeetingTransportation Research Board","topic":"Urban and Freight Transport Logistics","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"TRIPS architecture; Measure (data warehouse); Truck; Business; Geography; Transport engineering; Computer science; Engineering","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":[0.002169925,0.0009416254,0.0005105498,0.004772721,0.002237953,0.002085747,0.001780341,0.000677527,0.00210186],"category_scores_gemma":[0.009550943,0.0003690974,0.0008981421,0.01046342,0.0005014261,0.0009635566,0.0008993672,0.001211939,0.000448385],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01888714,"about_ca_system_score_gemma":0.03531133,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.985538,"about_ca_topic_score_gemma":0.9874248,"domain_scores_codex":[0.9984523,0.0001633259,0.00008599368,0.0002210172,0.0006798687,0.00039748],"domain_scores_gemma":[0.9884602,0.002083373,0.0006548895,0.000706746,0.007070167,0.001024656],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001063781,0.000482552,0.7413354,0.0004368176,0.0008951852,0.0003454383,0.001628821,0.07291191,0.002377658,0.006994665,0.09142724,0.0801006],"study_design_scores_gemma":[0.00008620875,0.0001081949,0.786869,0.0001400524,0.0002694379,0.00006739573,0.004922982,0.174998,0.002054166,0.002085001,0.02827083,0.0001287745],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8692862,0.0007721834,0.00671549,0.002027182,0.0002025942,0.0002945461,0.1095906,0.0008046635,0.01030654],"genre_scores_gemma":[0.9224214,0.0004174438,0.00972729,0.00025471,0.00004613679,0.0001817312,0.06349894,0.0001025937,0.003349707],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01888714,"threshold_uncertainty_score":0.1370364,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1655566658206615,"score_gpt":0.4166141207655523,"score_spread":0.2510574549448907,"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."}}