{"id":"W6945428317","doi":"10.25318/3610043901-eng","title":"Financial flows, life insurance business, quarterly, 1961 - 2012","year":2019,"lang":"en","type":"dataset","venue":"Statistics Canada Dissemination","topic":"German History and Society","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Life insurance; Table (database); Consumption (sociology); Capital (architecture); Life table; General insurance","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00009828453,0.0003092745,0.0003495701,0.00006394323,0.00042921,0.000130914,0.0002708583,0.0001522283,0.00332453],"category_scores_gemma":[0.0001847362,0.0003311989,0.00003897107,0.00005812652,0.0001048615,0.0001825915,0.00001832221,0.0003298573,0.000125767],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002913552,"about_ca_system_score_gemma":0.001411055,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.136506,"about_ca_topic_score_gemma":0.9164743,"domain_scores_codex":[0.9984453,0.0000478464,0.0003811792,0.0003057757,0.0005298507,0.0002900122],"domain_scores_gemma":[0.998626,0.0001556325,0.0003112552,0.0003597242,0.0004545193,0.00009287031],"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":[0.000008556956,0.00003678137,0.000002638517,0.0004619794,0.00002022857,0.00002079628,0.001255725,0.000003655023,5.697617e-7,0.004581392,0.9930417,0.0005659778],"study_design_scores_gemma":[0.0001162201,0.00003072749,0.00239988,0.00009556605,0.00006076015,9.124042e-7,0.0003064479,0.00001348473,1.861661e-7,0.0001625197,0.9964023,0.0004110112],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001277646,0.0003338427,0.00008117931,0.00007106821,0.005425248,0.0002356943,0.9934623,0.00001528677,0.000247654],"genre_scores_gemma":[0.001630977,0.00005360178,0.00003638291,0.0003163262,0.0009753709,0.00003098125,0.9878728,0.00002533747,0.00905819],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.7799683,"threshold_uncertainty_score":0.999914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009966025501330206,"score_gpt":0.2126259771730616,"score_spread":0.2026599516717313,"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."}}