{"id":"W4408329435","doi":"10.1002/fut.22572","title":"Joint Implied Willow Tree: An Approach for Joint S&P 500/VIX Calibration","year":2025,"lang":"en","type":"article","venue":"Journal of Futures Markets","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Joint (building); Willow; Economics; Calibration; Tree (set theory); Econometrics; Mathematics; Statistics; Biology; Botany; Engineering; Combinatorics","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":[0.003111646,0.0005359698,0.0006077178,0.001352738,0.0004897479,0.001198447,0.001216514,0.001440594,0.002274326],"category_scores_gemma":[0.009201002,0.0004920761,0.0008060901,0.001124583,0.0008575893,0.00214855,0.001506674,0.001678145,0.0005109615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004719583,"about_ca_system_score_gemma":0.001087378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001408764,"about_ca_topic_score_gemma":0.001647652,"domain_scores_codex":[0.9991645,0.0003463472,0.00004380488,0.0001299269,0.000253221,0.00006216698],"domain_scores_gemma":[0.9979386,0.0009529776,0.0003075098,0.0003916032,0.0003057004,0.0001035923],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001984409,0.000114231,0.006678696,0.0001074429,0.000114041,0.0002771055,0.0002462868,0.6206326,0.01351545,0.1767733,0.002063267,0.1792791],"study_design_scores_gemma":[0.000002877298,0.00001159355,0.0003921291,0.000007402401,0.000004471618,0.00003730027,0.00001004774,0.9813529,0.001137242,0.01624277,0.0007897232,0.00001171203],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01287068,0.00006231696,0.9861947,0.00006200698,0.000018706,0.0000118833,0.00002893956,0.0001403959,0.0006102798],"genre_scores_gemma":[0.4590944,0.0003545215,0.5364909,0.0001241831,0.0001087941,0.0001121027,0.0003768348,0.000283138,0.003055205],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003111646,"threshold_uncertainty_score":0.01645613,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03498723647639044,"score_gpt":0.3010388421064487,"score_spread":0.2660516056300582,"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."}}