{"id":"W4376644380","doi":"10.3390/logistics7020029","title":"Multiple Linear Regression Analysis of Canada’s Freight Transportation Framework","year":2023,"lang":"en","type":"article","venue":"Logistics","topic":"Maritime Ports and Logistics","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Cape Breton University","funders":"","keywords":"Truck; Revenue; Transport engineering; Product (mathematics); Business; Regression analysis; Computer science; Engineering; Finance","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004121887,0.00109019,0.0005666774,0.004034876,0.001732107,0.00303784,0.001547899,0.0003963579,0.006573753],"category_scores_gemma":[0.01247358,0.0003276686,0.00150307,0.008021143,0.0006228621,0.0008067856,0.001113987,0.001380996,0.0008520169],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03184256,"about_ca_system_score_gemma":0.05824864,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9922001,"about_ca_topic_score_gemma":0.9885002,"domain_scores_codex":[0.9973163,0.0006504447,0.0001052183,0.0005539507,0.0008652665,0.000508784],"domain_scores_gemma":[0.9918174,0.00204702,0.0004695052,0.0003828985,0.004937621,0.0003455797],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001877762,0.0001607039,0.7752715,0.0003112914,0.0009812848,0.0003573147,0.002213305,0.04485247,0.0004950365,0.03626203,0.06079995,0.0781073],"study_design_scores_gemma":[0.00004869176,0.0001041396,0.6956121,0.0004134413,0.0004125483,0.00015198,0.007031437,0.2025876,0.0007067249,0.006785262,0.08597043,0.000175679],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7618774,0.003370026,0.039354,0.004062343,0.0002835094,0.0008314389,0.1344813,0.001048781,0.05469113],"genre_scores_gemma":[0.9326221,0.001019979,0.02201489,0.0001998057,0.00002944431,0.00038735,0.03372414,0.0001447929,0.009857564],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03184256,"threshold_uncertainty_score":0.231035,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01940441425275219,"score_gpt":0.2319168366237963,"score_spread":0.2125124223710441,"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."}}