{"id":"W4388817788","doi":"10.26509/frbc-wp-202330","title":"Predictive Density Combination Using a Tree-Based Synthesis Function","year":2023,"lang":"en","type":"report","venue":"Working paper","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bank of Canada","funders":"Austrian Science Fund","keywords":"Interpretability; Pooling; Survey of Professional Forecasters; Computer science; Regression; Nonparametric regression; Range (aeronautics); Decision tree; Econometrics; Bayesian probability; Function (biology); Inflation (cosmology); Tree (set theory); Regression analysis; Machine learning; Artificial intelligence; Data mining; Mathematics; Statistics; Economics; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049312,0.0008910528,0.001344915,0.001909,0.0005968593,0.001740337,0.001344685,0.00145925,0.005526229],"category_scores_gemma":[0.01534547,0.0006986753,0.001463477,0.001480343,0.0007266665,0.002767983,0.001789087,0.001484612,0.0009798309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001165057,"about_ca_system_score_gemma":0.001024142,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003665927,"about_ca_topic_score_gemma":0.00293172,"domain_scores_codex":[0.9982601,0.0007919454,0.00009001538,0.0002648236,0.0004881156,0.0001050103],"domain_scores_gemma":[0.9934471,0.004858916,0.0003519309,0.0004269681,0.000809375,0.0001057157],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001062637,0.00003871557,0.0009991324,0.00008887947,0.00008768213,0.00008059342,0.0001517736,0.8091041,0.002057537,0.05388731,0.002047469,0.1313506],"study_design_scores_gemma":[0.000005208009,0.00001078515,0.0000858443,0.000008524724,0.00001076283,0.000009147997,0.000006373073,0.9860006,0.0003313443,0.01301992,0.0005049158,0.000006647919],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006767138,0.0001004591,0.991113,0.0001039266,0.00001939508,0.00003602618,0.00008168579,0.0002736461,0.001504793],"genre_scores_gemma":[0.481292,0.0003358619,0.5119942,0.0002346707,0.000153421,0.0004242547,0.0008061688,0.0003073345,0.004452046],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005526229,"threshold_uncertainty_score":0.026079,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1071883445524516,"score_gpt":0.2662259183882214,"score_spread":0.1590375738357698,"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."}}