{"id":"W6888939837","doi":"10.25318/3210033301-fra","title":"Éléments nutritifs à partir de l'approvisionnement alimentaire, selon la source de l'équivalent nutritif et les produits de base","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":"Base (topology); Homogeneous; Limiting","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":[0.000692478,0.001666951,0.00121649,0.005076669,0.0009002108,0.001795111,0.001733102,0.001131919,0.03832609],"category_scores_gemma":[0.00624591,0.0006127856,0.001339776,0.01364738,0.0004367105,0.0009185812,0.001022424,0.001435072,0.02761176],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006884498,"about_ca_system_score_gemma":0.01219459,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7095516,"about_ca_topic_score_gemma":0.811528,"domain_scores_codex":[0.9990221,0.00009480296,0.0001347357,0.0002501759,0.0003453718,0.0001528721],"domain_scores_gemma":[0.9961745,0.0007251944,0.0002596475,0.0003717254,0.002244471,0.000224568],"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":[0.0001460012,0.00002178967,0.006525139,0.001444314,0.00008253953,0.00002985831,0.00005187649,0.0003917697,0.0002041907,0.0006238667,0.9845847,0.005893901],"study_design_scores_gemma":[0.0001244741,0.00001241535,0.0320238,0.0004843682,0.00006308116,0.00004379304,0.0001742488,0.000272622,0.0003834327,0.0005078599,0.9658717,0.00003831828],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001824544,0.0001010872,0.00003205472,0.00004552815,0.00001207954,0.000005591906,0.9987994,0.00006196318,0.000759774],"genre_scores_gemma":[0.0009073446,0.0002224994,0.000299002,0.00005128416,0.000006347039,0.00003864739,0.9969543,0.00003404351,0.001486553],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2904484,"threshold_uncertainty_score":0.5843179,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01383597202462158,"score_gpt":0.2963954841610139,"score_spread":0.2825595121363924,"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."}}