{"id":"W4392225895","doi":"","title":"The logistics center as a leverage for regional economic development","year":2017,"lang":"fr","type":"preprint","venue":"","topic":"Economic Systems and Logistics Management","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministère des Transports","funders":"","keywords":"Leverage (statistics); Center (category theory); Business; Regional science; Engineering management; Computer science; Engineering; Geography; Artificial intelligence","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":[0.001375456,0.0003992561,0.0002519829,0.001690159,0.003228248,0.01242666,0.00112594,0.001738386,0.05834458],"category_scores_gemma":[0.002191411,0.0003037491,0.0004301378,0.002719553,0.003051155,0.00642447,0.01092892,0.002042371,0.006964844],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007786252,"about_ca_system_score_gemma":0.009732432,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01636534,"about_ca_topic_score_gemma":0.03413805,"domain_scores_codex":[0.9986538,0.0003682586,0.00002950219,0.0001601605,0.000231209,0.0005570914],"domain_scores_gemma":[0.9986089,0.0002191656,0.0001954782,0.000149846,0.0002206801,0.0006059875],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001246185,0.0000981592,0.007039755,0.000300773,0.00003550713,0.001353424,0.004441075,0.001320363,0.001033201,0.8236625,0.06286857,0.09772203],"study_design_scores_gemma":[0.00004165456,0.00008784847,0.004630311,0.0003088161,0.00003358738,0.0003513366,0.007440172,0.001025497,0.0007990372,0.028303,0.9569315,0.00004724395],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.07238406,0.003265581,0.00481707,0.02622004,0.0003501451,0.0001007864,0.0002786344,0.0003924601,0.8921913],"genre_scores_gemma":[0.8210074,0.003537521,0.002444245,0.002073183,0.0002791122,0.00008311383,0.0002004164,0.0001808285,0.1701941],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.05834458,"threshold_uncertainty_score":0.195182,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1279406151815536,"score_gpt":0.274308967319265,"score_spread":0.1463683521377114,"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."}}