{"id":"W2127737754","doi":"10.25336/p6vs46","title":"Support Vector Machines as tools for mortality graduation","year":2012,"lang":"en","type":"article","venue":"Canadian Studies in Population","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Graduation (instrument); Support vector machine; Nonparametric statistics; Parametric statistics; Computer science; Variety (cybernetics); Machine learning; Statistics; Econometrics; Artificial intelligence; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.001475673,0.0001243794,0.0001973732,0.0002475106,0.0005684409,0.00005704679,0.0001252732,0.00007606329,0.00004799655],"category_scores_gemma":[0.0009065425,0.0001357638,0.00007602996,0.000392777,0.0001403773,0.0007015025,0.00001788227,0.00006469397,0.00002091728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007480215,"about_ca_system_score_gemma":0.00009565611,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.3830743,"about_ca_topic_score_gemma":0.8949493,"domain_scores_codex":[0.9985034,0.0001387206,0.0003073157,0.0002002566,0.0002933021,0.000557026],"domain_scores_gemma":[0.9992983,0.0001044474,0.0001100689,0.0001746902,0.000124877,0.0001876361],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000003516278,0.0000166632,0.8949698,0.00002932335,0.00004825911,0.000001379612,0.006222515,0.00001541108,5.755883e-7,0.08373465,0.002330214,0.0126277],"study_design_scores_gemma":[0.000146741,0.00001379566,0.9630688,0.00001196901,0.00003631799,1.813424e-7,0.002670694,0.00002410602,0.000001497932,0.006800165,0.02706489,0.0001608044],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9812739,0.0005173146,0.00003526067,0.000847185,0.002571573,0.001140796,0.00005472837,0.00004947497,0.01350975],"genre_scores_gemma":[0.9981279,0.0001462229,0.0001730372,0.0003452131,0.0005655446,0.0002029981,0.0001379602,0.00001281169,0.000288342],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5118749,"threshold_uncertainty_score":0.6210338,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1680766289117301,"score_gpt":0.4362057539197982,"score_spread":0.268129125008068,"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."}}