{"id":"W6955504037","doi":"10.58079/ovfw","title":"Prédire le nombre de morts, suite","year":2020,"lang":"fr","type":"article","venue":"Industrias Culturais (Universidade de Coimbra)","topic":"Death, Funerary Practices, and Mourning","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Suite; Set (abstract data type); Software; Term (time); Domain (mathematical analysis)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008235673,0.0004596406,0.0005233984,0.0001269811,0.001480405,0.0005358674,0.0009966497,0.001127694,0.002195584],"category_scores_gemma":[0.0009547569,0.0005529665,0.0003386225,0.001745156,0.0006657684,0.003084169,0.0002400873,0.001799363,0.0005355766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006603568,"about_ca_system_score_gemma":0.002149775,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01073194,"about_ca_topic_score_gemma":0.000899204,"domain_scores_codex":[0.9958667,0.001008405,0.0003779177,0.0007639788,0.0006524827,0.001330534],"domain_scores_gemma":[0.9972816,0.0004056137,0.000493612,0.0003002854,0.00020115,0.001317784],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002431113,0.0003848139,0.004032519,0.0001410804,0.000608296,0.002375372,0.1473481,0.001162718,0.003788377,0.08896161,0.641615,0.1093389],"study_design_scores_gemma":[0.001486159,0.000160535,0.005472757,0.0001512102,0.0002969772,0.00008294981,0.04558288,0.000956575,0.0003545664,0.0001445999,0.9446436,0.0006672039],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1648233,0.004038368,0.0003615303,0.4213041,0.001576675,0.000604675,0.0001584746,0.0003867202,0.4067462],"genre_scores_gemma":[0.5375946,0.001127787,0.001027892,0.006551775,0.00362429,0.000009653655,0.0000878475,0.00007779369,0.4498984],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.4147523,"threshold_uncertainty_score":0.9998195,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06177102852131362,"score_gpt":0.2794469531572858,"score_spread":0.2176759246359722,"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."}}