{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001944531,0.0003988791,0.0009876189,0.0003485922,0.00003448565,0.00008321201,0.0004076326,0.0004373724,0.00004397679],"category_scores_gemma":[0.000593546,0.0004152921,0.00006042451,0.0001693284,0.0004123016,0.00007993366,0.0004404756,0.001950787,0.000003014595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002027832,"about_ca_system_score_gemma":0.0001602003,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01146091,"about_ca_topic_score_gemma":0.3390417,"domain_scores_codex":[0.9969247,0.0004898435,0.0008581512,0.0007812358,0.0001983107,0.0007477643],"domain_scores_gemma":[0.99823,0.0006895363,0.00006269495,0.000717097,0.00006377804,0.0002368315],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001750297,0.000876852,0.5527742,0.0004883854,0.0001664636,0.02923197,0.007417935,0.3153673,0.000005096092,0.0001532818,0.00008691096,0.09325662],"study_design_scores_gemma":[0.006691479,0.001827408,0.3887579,0.0004227982,0.0001190894,0.0007954342,0.08432598,0.4915031,0.00001267687,0.003810897,0.01749295,0.004240266],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9803379,0.002109282,0.0001206489,0.00003439849,0.0003183317,0.001175658,0.0001108029,0.00008849162,0.01570446],"genre_scores_gemma":[0.9792954,0.01624127,0.003944294,0.00001096716,0.0001210213,0.0001841472,0.00004601768,0.00006178234,0.00009505211],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3275808,"threshold_uncertainty_score":0.9998299,"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."}}