{"id":"W6926678402","doi":"10.25318/1610002201-fra","title":"Production des minéraux métalliques, non-métalliques, agrégats, argile et minéraux réfractaires en quantité, annuel","year":2021,"lang":"fr","type":"dataset","venue":"Statistics Canada Dissemination","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Production (economics); Population; Identification (biology); Selection (genetic algorithm)","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":[0.0007989518,0.001708108,0.001448568,0.004879021,0.001045858,0.002749129,0.00194566,0.001191794,0.02519388],"category_scores_gemma":[0.00569837,0.0007192128,0.001195484,0.01326165,0.0005110974,0.0009824754,0.0009520461,0.001827109,0.01800476],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008029777,"about_ca_system_score_gemma":0.01356852,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8447343,"about_ca_topic_score_gemma":0.9020451,"domain_scores_codex":[0.999189,0.00005355279,0.00007385375,0.0002290453,0.0002761481,0.0001785364],"domain_scores_gemma":[0.996537,0.0005445898,0.0003106639,0.0003402631,0.001992568,0.0002749839],"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":[0.0000652352,0.00001824846,0.007058596,0.0007852539,0.00006625694,0.00002066552,0.00004598483,0.0005352771,0.0001170889,0.000678626,0.9874658,0.003142981],"study_design_scores_gemma":[0.000183646,0.00001442478,0.05603657,0.000609767,0.00007935724,0.00007293287,0.0002456197,0.000964511,0.0005129715,0.001028747,0.9401887,0.00006282555],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001900149,0.000082954,0.00002341015,0.00004503899,0.000009630034,0.000003040475,0.9991966,0.00005301588,0.0003963161],"genre_scores_gemma":[0.001058314,0.0001872482,0.0002097456,0.00003403064,0.000006195148,0.00002280921,0.9971192,0.00002708532,0.001335419],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1552657,"threshold_uncertainty_score":0.3123602,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01024269759292437,"score_gpt":0.2970873513774342,"score_spread":0.2868446537845098,"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."}}