{"id":"W3177259224","doi":"10.3934/qfe.2021024","title":"Learning about financial health in Canada","year":2021,"lang":"en","type":"article","venue":"Quantitative Finance and Economics","topic":"Health disparities and outcomes","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; Wilfrid Laurier University","funders":"","keywords":"Payroll; Demographics; Perspective (graphical); Cluster (spacecraft); Coping (psychology); Point (geometry); Cluster analysis; Psychology; Financial distress; Demographic economics; Actuarial science; Business; Economics; Demography; Sociology; Accounting; Computer science; Clinical psychology; Artificial intelligence; Mathematics; Financial system","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.00372539,0.0002036839,0.0003283519,0.00224308,0.008455767,0.005627124,0.001106372,0.0008371789,0.008623297],"category_scores_gemma":[0.01863996,0.0001640783,0.0002516422,0.004467644,0.003222979,0.001886825,0.002745654,0.002090708,0.0002673525],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.08264097,"about_ca_system_score_gemma":0.1440478,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9902287,"about_ca_topic_score_gemma":0.9928887,"domain_scores_codex":[0.996693,0.0005408243,0.0000728708,0.0002350453,0.001297089,0.001161116],"domain_scores_gemma":[0.991671,0.001289419,0.0008071443,0.0002050266,0.0030756,0.002951776],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001367456,0.000156892,0.4809955,0.0002166322,0.00008467113,0.0003889991,0.06483463,0.001244906,0.0002205088,0.05289082,0.158957,0.2398728],"study_design_scores_gemma":[0.00003071493,0.00006995604,0.5998846,0.0008167984,0.00004668386,0.0001340954,0.1919563,0.002862268,0.000365661,0.02007975,0.1836025,0.0001507941],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6874315,0.004781089,0.001131943,0.1998404,0.0005688673,0.0001202087,0.005154425,0.00006502787,0.1009065],"genre_scores_gemma":[0.98813,0.002176111,0.0005824359,0.003269898,0.00007185226,0.00001801912,0.0006458272,0.00001169232,0.005094202],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08264097,"threshold_uncertainty_score":0.599605,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03060984997730092,"score_gpt":0.3163007043122275,"score_spread":0.2856908543349266,"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."}}