{"id":"W7000363504","doi":"","title":"Evauation du capital humain : pour une meilleure exploitation des ressources humaines au Canada /","year":2015,"lang":"fr","type":"other","venue":"Bibliothèque et Archives nationales du Québec (Québec government)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Capital (architecture); Government (linguistics)","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":[0.001776134,0.000578477,0.0004556368,0.003210263,0.006566455,0.006209514,0.001517992,0.002160579,0.03894111],"category_scores_gemma":[0.003821901,0.0003422708,0.0008199891,0.005120471,0.001481221,0.001252203,0.001872532,0.001924032,0.002501856],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.07426646,"about_ca_system_score_gemma":0.1927002,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9950977,"about_ca_topic_score_gemma":0.9966398,"domain_scores_codex":[0.997624,0.0001155676,0.00004620554,0.000140299,0.001260015,0.0008139267],"domain_scores_gemma":[0.9972188,0.0001431707,0.00009436948,0.0001267841,0.001886961,0.0005298846],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002289516,0.0001071634,0.01919942,0.0006830943,0.00009835295,0.0006736522,0.003202386,0.001292261,0.001343004,0.1118233,0.6595265,0.201822],"study_design_scores_gemma":[0.0000239708,0.00001488909,0.03508649,0.0003551556,0.00003196719,0.0001065115,0.001749292,0.0003609031,0.001012783,0.002052592,0.9591626,0.00004276108],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.0812617,0.04521366,0.004787699,0.07715957,0.002233174,0.0005718903,0.03610825,0.0009303045,0.7517337],"genre_scores_gemma":[0.155009,0.00936974,0.004959264,0.004689716,0.0001814371,0.0001640386,0.004143019,0.0003361674,0.8211477],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9257336,"threshold_uncertainty_score":0.5388434,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01484903504764735,"score_gpt":0.22813895599651,"score_spread":0.2132899209488626,"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."}}