{"id":"W7106709396","doi":"10.34989/drb0-zr06","title":"Supplemental material for \"Calculating Effective Degrees of Freedom for Forecast Combinations and Ensemble Models\"","year":2025,"lang":"en","type":"dataset","venue":"Bank of Canada Research","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bank of Canada","funders":"","keywords":"Degrees of freedom (physics and chemistry); Key (lock); Ensemble forecasting; Stability (learning theory)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00204008,0.0003136828,0.0007789112,0.0007275123,0.0003488652,0.00006311524,0.0005974226,0.0002274111,0.0001182604],"category_scores_gemma":[0.001135465,0.0003319174,0.0001237006,0.0003991192,0.000352334,0.0001257222,0.0004509688,0.0003018398,3.406949e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008973732,"about_ca_system_score_gemma":0.00344544,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7014181,"about_ca_topic_score_gemma":0.9417968,"domain_scores_codex":[0.9962294,0.0003137793,0.0006663769,0.0005479013,0.001382324,0.0008602057],"domain_scores_gemma":[0.994271,0.002999123,0.0002889319,0.0005654256,0.001731678,0.0001438513],"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":[0.0008933549,0.0001345885,0.000005385853,0.003050931,0.0002769066,0.000002553394,0.00002495663,0.0007423677,0.0009939201,0.002412925,0.9913349,0.0001272167],"study_design_scores_gemma":[0.01177858,0.002167984,0.000107161,0.00130561,0.0004461346,0.000008324771,0.000851124,0.09083361,0.01528246,0.002803552,0.8735616,0.0008538751],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002607592,0.00008435939,0.0001625265,0.00007107621,0.0002604652,0.005687949,0.9910137,0.0000114068,0.0001009298],"genre_scores_gemma":[0.02215212,0.000005381423,0.001981495,0.000003406552,0.00009619613,0.0019476,0.9735811,0.00009786779,0.0001348025],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2403787,"threshold_uncertainty_score":0.9999133,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04984682202529575,"score_gpt":0.3567672840015437,"score_spread":0.3069204619762479,"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."}}