{"id":"W6945150405","doi":"10.25318/9810056501-fra","title":"Indicateurs de qualité des données du questionnaire détaillé pour la mobilité : Canada, provinces et territoires, régions métropolitaines de recensement, agglomérations de recensement et subdivisions de recensement","year":2023,"lang":"fr","type":"dataset","venue":"Statistics Canada Dissemination","topic":"Biological and pharmacological studies of plants","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Population; Public health; Census","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.002381467,0.0008201285,0.000814109,0.0001760427,0.001331017,0.000143595,0.0005622525,0.0005001015,0.0002319503],"category_scores_gemma":[0.01027369,0.0007270902,0.0001007297,0.0005177002,0.0005813406,0.00009506474,0.0003071775,0.001087529,0.00001597043],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.009557919,"about_ca_system_score_gemma":0.008044837,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.973344,"about_ca_topic_score_gemma":0.9983786,"domain_scores_codex":[0.9926438,0.002456039,0.001247599,0.0008987883,0.001040672,0.001713121],"domain_scores_gemma":[0.99229,0.004898074,0.0007004737,0.0003990134,0.0005879499,0.001124456],"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":[0.0001391641,0.000704205,0.0287364,0.001190429,0.0004022144,0.001299909,0.0004810991,0.0001898493,0.0004470645,0.004526494,0.9515938,0.01028943],"study_design_scores_gemma":[0.0008446776,0.0005221589,0.4669864,0.002534589,0.00094553,0.0001555609,0.003694248,0.0008605949,0.0002386417,0.004943374,0.5172825,0.0009916996],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.04118457,0.00228484,0.004891645,0.04103331,0.001048056,0.001524654,0.9078004,0.0001193648,0.0001131823],"genre_scores_gemma":[0.1065882,0.04404081,0.009106431,0.005039355,0.0006650125,0.001124732,0.8310063,0.0001104022,0.002318754],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.4382501,"threshold_uncertainty_score":0.9999691,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0282702588617985,"score_gpt":0.3295212327250271,"score_spread":0.3012509738632286,"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."}}