{"id":"W4392635822","doi":"","title":"Evolution of warehouse location from 2012 to 2019 in major U.S. metropolitan areas","year":2022,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Advanced Manufacturing and Logistics Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministère des Transports","funders":"","keywords":"Metropolitan area; Warehouse; Computer science; Business; Geography; Marketing","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.0004958627,0.0002289481,0.0001861379,0.001297411,0.000274489,0.001236484,0.0007048929,0.0004533415,0.003427467],"category_scores_gemma":[0.00259651,0.0001641803,0.0004469276,0.003470478,0.0002051966,0.0009160846,0.0008762176,0.0005101743,0.001133895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001967504,"about_ca_system_score_gemma":0.001485546,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1830927,"about_ca_topic_score_gemma":0.3195324,"domain_scores_codex":[0.9997013,0.00004410667,0.00002554934,0.00008352002,0.00006169527,0.00008373224],"domain_scores_gemma":[0.9983509,0.0001596857,0.0004794865,0.00005172302,0.0008103204,0.0001478958],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002611724,0.00005037564,0.9385024,0.0001055484,0.0001282702,0.0001893142,0.0005360151,0.005245769,0.0006098346,0.001027857,0.02939457,0.02394885],"study_design_scores_gemma":[0.000007015954,0.00003961643,0.9771305,0.00005299076,0.00003609495,0.0001297389,0.002248986,0.006260144,0.0003318679,0.0001978531,0.01354731,0.00001792113],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.95302,0.0007867395,0.000962582,0.002525792,0.0001008421,0.00001444774,0.03660663,0.0001751421,0.005807746],"genre_scores_gemma":[0.9791099,0.0003296744,0.0005094273,0.0001574567,0.00002422558,0.00001684993,0.01763309,0.00002551774,0.002193776],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1830927,"threshold_uncertainty_score":0.364054,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006759510721707667,"score_gpt":0.1981434581743707,"score_spread":0.1913839474526631,"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."}}