{"id":"W3149254007","doi":"10.21314/jfmi.2021.003","title":"Predicting payment migration in Canada","year":2021,"lang":"en","type":"article","venue":"The Journal of Financial Market Infrastructures","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bank of Canada","funders":"","keywords":"Settlement (finance); Payment; Collateral; Clearing; Counterfactual thinking; Payment system; Business; Value (mathematics); Database transaction; Actuarial science; Transaction cost; Finance; Economics; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001817205,0.00005856038,0.00009451318,0.00004128742,0.00003154733,0.000008104234,0.0000763917,0.00002248431,0.0001581699],"category_scores_gemma":[0.0001038911,0.00004433088,0.00002143319,0.0002123896,0.000009676012,0.00006707389,0.000003676289,0.0002031464,1.017901e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000158807,"about_ca_system_score_gemma":0.0006796047,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.0377564,"about_ca_topic_score_gemma":0.8813369,"domain_scores_codex":[0.9993491,0.00003049035,0.0003487008,0.00002803908,0.0001527223,0.00009094566],"domain_scores_gemma":[0.9996774,0.00006532518,0.00006807394,0.00006869047,0.00009813357,0.00002237392],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001216012,0.00002234933,0.5700734,0.0000929313,0.00005959222,0.00009862584,0.002699598,0.3333353,0.01202316,0.0007949381,0.04759178,0.03308674],"study_design_scores_gemma":[0.000173938,0.000006990954,0.9921002,0.00002688975,0.0000104997,0.00003200233,0.0002420087,0.00103291,0.002560263,0.0005541274,0.003215993,0.00004414509],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975598,0.0001786587,0.0007294352,0.0002865256,0.0005666388,0.00003696284,0.00001455981,0.000005943828,0.0006214959],"genre_scores_gemma":[0.9993528,0.00008959921,0.000300029,0.0001524107,0.00008513256,9.217893e-7,0.000002870318,0.000004754567,0.0000115298],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8435805,"threshold_uncertainty_score":0.9686512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003364132960477012,"score_gpt":0.1776692560830296,"score_spread":0.1743051231225526,"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."}}