{"id":"W6957757656","doi":"10.6068/dp14ba7bcd6ce5","title":"Trend 2009 - 2010. Statistics Canada. CANSIM: Transportation - Transportation by Road | Country: Canada | Table: Trucking revenue distribution by type of product hauled and territory | Variable: All other goods | Units: $CAD x 1,000, 2009-2010. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-197.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Product (mathematics); Revenue; Descriptive statistics; Economic statistics; Summary statistics; Distribution (mathematics); Official statistics; Taxis; Census","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":[0.001795458,0.002451819,0.002507736,0.007370589,0.002982709,0.004471172,0.004967086,0.001381311,0.08827808],"category_scores_gemma":[0.01506544,0.001635343,0.002085099,0.03709052,0.0006043804,0.002631424,0.002070326,0.002947341,0.05344537],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05104829,"about_ca_system_score_gemma":0.1169297,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9947556,"about_ca_topic_score_gemma":0.9927361,"domain_scores_codex":[0.996317,0.0002357361,0.0003799901,0.0004990067,0.001721341,0.0008468685],"domain_scores_gemma":[0.9733654,0.0008423062,0.0007481888,0.0007495229,0.02308809,0.001206476],"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":[0.00002184579,0.000006100692,0.0009803536,0.0002279743,0.00001971122,0.000006780419,0.00001787121,0.0001424546,0.000009450153,0.0004069837,0.9965579,0.001602733],"study_design_scores_gemma":[0.0001364488,0.00001225414,0.02114327,0.0008243365,0.00007182809,0.00002832455,0.0004345517,0.0006483766,0.0001865392,0.0006928697,0.9757364,0.00008481831],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005704466,0.00005528383,0.00003037644,0.000125827,0.00003067495,0.00001311174,0.9986324,0.00006139681,0.0009939873],"genre_scores_gemma":[0.0009534027,0.0003184511,0.0003821493,0.0001528201,0.00001755531,0.000102115,0.9938595,0.0001235235,0.004090553],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08827808,"threshold_uncertainty_score":0.370383,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02387981064249483,"score_gpt":0.2484361326824904,"score_spread":0.2245563220399956,"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."}}