{"id":"W4392630125","doi":"","title":"An analysis of the evolution of warehouses location from 2012 to 2019 in major U.S. Consolidated Statistical Areas (CSA): New insights of warehousing spatial patterns","year":2022,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Consumer Retail Behavior Studies","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministère des Transports","funders":"","keywords":"Computer science; Data warehouse; Warehouse; Data science; Statistical analysis; Data mining; Business; Statistics; Marketing; Mathematics","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":[0.0006043781,0.0001876582,0.0001879366,0.002225847,0.0002951329,0.001245755,0.0005191873,0.0004394079,0.001975719],"category_scores_gemma":[0.00266614,0.0001695717,0.0004796176,0.006681718,0.0002533979,0.0009928528,0.0009138115,0.0004690733,0.000640418],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001457445,"about_ca_system_score_gemma":0.001746366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2016217,"about_ca_topic_score_gemma":0.3098337,"domain_scores_codex":[0.9995554,0.00005542381,0.00005835227,0.0001207747,0.0001095379,0.0001004788],"domain_scores_gemma":[0.9973653,0.0003862676,0.000903391,0.0001080365,0.001026299,0.0002107358],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007244071,0.00003287157,0.9849439,0.00004068484,0.00006153987,0.00008801521,0.0005382428,0.001117776,0.0002443851,0.0003888828,0.004101951,0.00836932],"study_design_scores_gemma":[0.000001429461,0.00002817553,0.9911256,0.00002525169,0.00001793438,0.00006328423,0.002255635,0.002920106,0.0001599476,0.00007044371,0.003323772,0.000008437921],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9788772,0.0002496727,0.000456572,0.000652899,0.00002159797,0.00001143404,0.01784059,0.00004377926,0.00184623],"genre_scores_gemma":[0.9879026,0.0001901185,0.0003452343,0.0000604937,0.00001078231,0.00001286549,0.01072283,0.00001032321,0.0007446451],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2016217,"threshold_uncertainty_score":0.4008964,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01311207346516949,"score_gpt":0.2280877018926722,"score_spread":0.2149756284275027,"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."}}