{"id":"W2471601219","doi":"10.3917/qdm.161.0011","title":"Prévision des mouvements d’employés par matrices actuarielles et réseaux de neurones artificiels : une application aux données bancaires en contexte canadien","year":2016,"lang":"fr","type":"article","venue":"Question(s) de management","topic":"Firm Innovation and Growth","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Humanities; Art; Political science","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":[0.001919701,0.0005494823,0.000783637,0.001355065,0.0004189091,0.00124583,0.0009268302,0.00101459,0.003147375],"category_scores_gemma":[0.009298141,0.0003125395,0.001034365,0.00154611,0.0005901159,0.0009307593,0.0007060929,0.0009144451,0.0003347649],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001140389,"about_ca_system_score_gemma":0.001184946,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08974446,"about_ca_topic_score_gemma":0.0751566,"domain_scores_codex":[0.9993089,0.0002088957,0.00004318829,0.0001804083,0.0001972454,0.00006118984],"domain_scores_gemma":[0.9959955,0.003127009,0.0001887183,0.0002083448,0.0004220885,0.00005831348],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004110338,0.00008830345,0.03633419,0.0007047195,0.0003377773,0.000312287,0.001433752,0.6968918,0.0142233,0.01888569,0.001623261,0.2287538],"study_design_scores_gemma":[0.0000263359,0.0000945377,0.02476024,0.0001139149,0.00005800491,0.0001313691,0.0007086055,0.9445913,0.006125009,0.01763994,0.005650204,0.0001006504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3884994,0.002862954,0.5992846,0.0009558487,0.00008374213,0.0000989109,0.001634873,0.0006042967,0.005975462],"genre_scores_gemma":[0.8361853,0.001055085,0.1568155,0.00009150031,0.00002654786,0.0001038482,0.000790948,0.00007014856,0.00486117],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9102556,"threshold_uncertainty_score":0.1784441,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02275913989665141,"score_gpt":0.25559702639552,"score_spread":0.2328378864988686,"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."}}