{"id":"W6888913616","doi":"10.25318/2710027701-fra","title":"Adoption des technologies de pointe de manutention du matériel, de chaîne d'approvisionnement et de logistique, selon l'industrie et la taille de l'entreprise","year":2019,"lang":"fr","type":"dataset","venue":"Statistics Canada Dissemination","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Work (physics); Statistical analysis; Field (mathematics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001197725,0.001490142,0.001045935,0.005831194,0.0009613086,0.002010834,0.002076953,0.001463905,0.01980003],"category_scores_gemma":[0.009977181,0.000594542,0.001153536,0.01265002,0.0004467135,0.001332101,0.001355207,0.001830585,0.01643259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008789422,"about_ca_system_score_gemma":0.01475321,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7680073,"about_ca_topic_score_gemma":0.850046,"domain_scores_codex":[0.998562,0.0001344564,0.0001784068,0.0002828456,0.0005210151,0.0003212371],"domain_scores_gemma":[0.9933348,0.001098208,0.0006830208,0.000509095,0.003863776,0.0005110838],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008121201,0.0000240855,0.009361993,0.000607751,0.00004598283,0.00001842093,0.00008125145,0.0004106486,0.00007257215,0.0008707562,0.9852011,0.003224154],"study_design_scores_gemma":[0.0001858902,0.00001957785,0.1001239,0.0006738018,0.00007143472,0.00004654885,0.0005345842,0.0008991614,0.0003812849,0.000843111,0.8961461,0.00007462387],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0004637257,0.00006815056,0.00003509125,0.00007324643,0.00001293803,0.000009578501,0.9984628,0.00004419215,0.0008302552],"genre_scores_gemma":[0.001591547,0.0001230822,0.0002195615,0.00004647325,0.00000745706,0.00006643921,0.9956166,0.00002129536,0.002307616],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2319927,"threshold_uncertainty_score":0.4667178,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01224301449934486,"score_gpt":0.2894512105470922,"score_spread":0.2772081960477473,"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."}}