{"id":"W4234091850","doi":"10.1515/iupac.81.0521","title":"Life Table (in Actuarial Science)","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Glossary; Ecotoxicology; Table (database); Relation (database); Computer science; Data science; Ecology; Biology; Data mining; Linguistics; Philosophy","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001924679,0.001229751,0.001030833,0.004120936,0.0004573988,0.002773887,0.00214117,0.001675591,0.2125074],"category_scores_gemma":[0.02038565,0.000535909,0.001641665,0.006069586,0.0002866925,0.00190769,0.001630252,0.002308548,0.1604995],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001232453,"about_ca_system_score_gemma":0.001707233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01167533,"about_ca_topic_score_gemma":0.01641979,"domain_scores_codex":[0.9983134,0.0003785524,0.0002692709,0.0004394545,0.000409695,0.0001894594],"domain_scores_gemma":[0.9920171,0.003609408,0.00103533,0.001654876,0.001286915,0.0003963503],"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":[0.00005747641,0.00001941332,0.002690076,0.000493088,0.00006388579,0.00001604008,0.00001194675,0.0008544511,0.00001506885,0.0009900815,0.9865223,0.008266187],"study_design_scores_gemma":[0.0003013255,0.00004446662,0.007681906,0.0006193118,0.00005871386,0.000169542,0.00006054714,0.002163256,0.0001303152,0.006511328,0.9822189,0.00004044182],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002125498,0.0001722726,0.0002545427,0.0001529362,0.00004487854,0.00001297196,0.9968151,0.0004186572,0.001916092],"genre_scores_gemma":[0.002510959,0.0002868525,0.0006462935,0.0002025778,0.00006117621,0.0001041168,0.9933614,0.0001350818,0.002691547],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2125074,"threshold_uncertainty_score":0.7109082,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01811467069594528,"score_gpt":0.4292849115059548,"score_spread":0.4111702408100095,"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."}}