{"id":"W2604081358","doi":"","title":"Logistics sprawl in North America: methodological issues and a case study in Toronto","year":2015,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Urban and Freight Transport Logistics","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Urban sprawl; Atlanta; Metropolitan area; Geography; Regional science; City logistics; Transport engineering; Environmental planning; Identification (biology); Business; Economic geography; Land use; Engineering; Civil engineering; Archaeology","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":[0.001349796,0.0004920365,0.0003371612,0.002421242,0.005895985,0.002225776,0.001266109,0.001035662,0.002421],"category_scores_gemma":[0.003084962,0.0005235047,0.0006330289,0.01023701,0.002412731,0.0009225827,0.002161533,0.0009782419,0.0001793456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05073258,"about_ca_system_score_gemma":0.01522289,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9575394,"about_ca_topic_score_gemma":0.9831722,"domain_scores_codex":[0.998323,0.0006714524,0.00008908181,0.0001703919,0.0002233576,0.0005225877],"domain_scores_gemma":[0.997281,0.001195344,0.000389307,0.0001609068,0.0005862932,0.000387036],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000705739,0.001571052,0.4917847,0.00152901,0.0002826046,0.02427555,0.33993,0.02281542,0.003381099,0.03311761,0.01492369,0.06568348],"study_design_scores_gemma":[0.00007773686,0.0004289126,0.452858,0.0005147443,0.0002006498,0.001223059,0.5012177,0.0105118,0.001198724,0.001565034,0.03010483,0.00009893953],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9881256,0.001002998,0.001326411,0.0007879958,0.00001484039,0.0002471429,0.000781657,0.000009916253,0.007703425],"genre_scores_gemma":[0.990568,0.001505952,0.002151998,0.0002176108,0.00001385255,0.0002512247,0.0007799456,0.00001187783,0.004499614],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05073258,"threshold_uncertainty_score":0.3680924,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.189936896731583,"score_gpt":0.3795846063650302,"score_spread":0.1896477096334472,"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."}}