{"id":"W2146224013","doi":"10.7202/1011540ar","title":"Microsimuler l’avenir des retraites en France : l’exemple du modèle Destinie","year":2012,"lang":"fr","type":"article","venue":"Cahiers québécois de démographie","topic":"Retirement, Disability, and Employment","field":"Social Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Humanities; Political science; Art","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":["sts"],"category_scores_codex":[0.002898466,0.0005998303,0.0005833734,0.0002586042,0.001824261,0.0001768287,0.0008158408,0.0008058895,0.0007820822],"category_scores_gemma":[0.0008857979,0.0006538688,0.0006633424,0.001993166,0.008672932,0.0009072308,0.0001165195,0.0007148684,0.0002252408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008691487,"about_ca_system_score_gemma":0.0003840509,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.0413959,"about_ca_topic_score_gemma":0.02196315,"domain_scores_codex":[0.9942358,0.0009616081,0.0007001919,0.0007138593,0.0007253248,0.002663284],"domain_scores_gemma":[0.99683,0.000812763,0.0002649395,0.0006700589,0.0002164967,0.00120576],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003333066,0.0009033217,0.8327488,0.0003029855,0.0001877192,0.00001018784,0.09806537,0.00003552315,0.0007874587,0.0399438,0.01158231,0.01539921],"study_design_scores_gemma":[0.0007254217,0.0002107263,0.3089979,0.0002416541,0.0003068943,0.00001695614,0.01169583,0.00005481418,0.0009073056,0.01392787,0.6618643,0.001050355],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9130407,0.056339,0.0005586234,0.003595184,0.002831526,0.0006946949,0.0001046121,0.000208832,0.02262683],"genre_scores_gemma":[0.9766993,0.009820827,0.002739715,0.001315053,0.001987746,0.0001206756,0.00002191468,0.00009326424,0.00720151],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.650282,"threshold_uncertainty_score":0.9995912,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07574930250570512,"score_gpt":0.3282387090686679,"score_spread":0.2524894065629628,"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."}}