{"id":"W3143584225","doi":"10.7202/1076123ar","title":"L’assurance vie : un atout pour lutter contre la pauvreté monétaire des travailleurs dans la CEMAC","year":2021,"lang":"fr","type":"article","venue":"Assurances et gestion des risques","topic":"Banking stability, regulation, efficiency","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Political science; Humanities; Economics; Philosophy","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.004335724,0.000546627,0.0006353729,0.00108056,0.001593324,0.003441383,0.001008859,0.001242961,0.01204755],"category_scores_gemma":[0.01469623,0.0003031956,0.0007097039,0.001352093,0.001355346,0.001907329,0.002495057,0.002355589,0.001289956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003739435,"about_ca_system_score_gemma":0.006851567,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05677321,"about_ca_topic_score_gemma":0.07648093,"domain_scores_codex":[0.9965096,0.001530069,0.0000904074,0.000366784,0.0008311888,0.0006719549],"domain_scores_gemma":[0.992106,0.002728413,0.001621214,0.0004853747,0.001589655,0.001469418],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002267203,0.000699562,0.3160616,0.002486773,0.0005359857,0.0006904413,0.01954535,0.007144365,0.00529872,0.04817235,0.04575843,0.5513393],"study_design_scores_gemma":[0.0001684596,0.001888213,0.671855,0.002803041,0.0004165086,0.0003888877,0.02408761,0.008206964,0.004204493,0.01454803,0.2711607,0.0002721324],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8383723,0.02144665,0.02167201,0.0470546,0.001691114,0.0002779066,0.003220637,0.0007957691,0.06546895],"genre_scores_gemma":[0.9722939,0.002910883,0.006034962,0.001607229,0.0002807657,0.0001368824,0.0006415349,0.00009484273,0.01599911],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05677321,"threshold_uncertainty_score":0.1128855,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02261809737547102,"score_gpt":0.2459877246481342,"score_spread":0.2233696272726632,"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."}}