{"id":"W3136250670","doi":"","title":"Compter les morts? Une analyse de la mortalité excédentaire récente en temps de pandémie","year":2020,"lang":"fr","type":"article","venue":"","topic":"COVID-19 and healthcare impacts","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Humanities; Coronavirus disease 2019 (COVID-19); Philosophy; Political science; Medicine","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":[0.003540291,0.0003223311,0.000429374,0.001312121,0.0005971632,0.001838078,0.0004469478,0.0006767861,0.004133411],"category_scores_gemma":[0.01538024,0.0003132131,0.001147718,0.002191709,0.0009749783,0.002172426,0.0008990527,0.001472236,0.0008896387],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008955952,"about_ca_system_score_gemma":0.001506989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02103124,"about_ca_topic_score_gemma":0.03198718,"domain_scores_codex":[0.9975927,0.001055871,0.0001861357,0.0003928273,0.0004496886,0.0003228146],"domain_scores_gemma":[0.9923375,0.003120141,0.002640595,0.0003843081,0.001155301,0.0003621743],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009754598,0.00008212138,0.8214185,0.001268975,0.001231941,0.0007903606,0.007496091,0.001627503,0.0007237283,0.007985386,0.01549971,0.1409003],"study_design_scores_gemma":[0.00001354928,0.0003575056,0.9612752,0.0008239493,0.0002594477,0.0005248826,0.005099427,0.0005002884,0.0002604498,0.001694021,0.02914668,0.00004463391],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8993669,0.0485175,0.00525975,0.02192327,0.001037654,0.00007439534,0.007110151,0.0000717422,0.01663867],"genre_scores_gemma":[0.9589568,0.02437387,0.001764178,0.002057489,0.0008825073,0.00008648675,0.003237722,0.00005043034,0.00859041],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02103124,"threshold_uncertainty_score":0.04181761,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06252773405026005,"score_gpt":0.413739618040764,"score_spread":0.351211883990504,"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."}}