{"id":"W4414142751","doi":"10.3390/ijfs13030170","title":"Predicting the Canadian Yield Curve Using Machine Learning Techniques","year":2025,"lang":"en","type":"article","venue":"International Journal of Financial Studies","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Lasso (programming language); Yield curve; Yield (engineering); Hyperparameter; Feature (linguistics); Set (abstract data type); Government (linguistics)","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":[0.001162134,0.00007952214,0.0002244011,0.0002721954,0.0002927415,0.00007305408,0.0003167217,0.00005058119,0.00002789776],"category_scores_gemma":[0.002445484,0.00006800375,0.0001107463,0.000132753,0.00006395104,0.0001187428,0.00008563749,0.0003208216,0.00000115623],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003964072,"about_ca_system_score_gemma":0.0001452694,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.04052096,"about_ca_topic_score_gemma":0.09026765,"domain_scores_codex":[0.9991397,0.00001780577,0.0005495549,0.00009909119,0.00006482118,0.0001290831],"domain_scores_gemma":[0.998979,0.0001475348,0.0004403621,0.00006289635,0.0003412087,0.0000289684],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002319715,0.00001453163,0.9639579,0.000007542392,0.0001891744,0.00001227839,0.0003588122,0.00004168499,0.000008739088,0.03190965,0.0002877884,0.003188679],"study_design_scores_gemma":[0.0006560532,0.0001616208,0.457891,0.0006701778,0.00005489718,0.00006134746,0.0004023462,0.05325951,0.0002988577,0.1860771,0.3000486,0.0004183773],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9359218,0.01682633,0.004991322,0.009928706,0.005034776,0.0001925863,0.0001317818,0.00002153657,0.02695111],"genre_scores_gemma":[0.9977298,0.0007055342,0.0005628043,0.000381309,0.0003006092,0.000002009815,9.566608e-7,0.000004678179,0.0003123171],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5060669,"threshold_uncertainty_score":0.9658683,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0468335649355027,"score_gpt":0.292301633986744,"score_spread":0.2454680690512413,"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."}}