{"id":"W4408329435","doi":"10.1002/fut.22572","title":"Joint Implied Willow Tree: An Approach for Joint S&amp;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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002495457,0.0001891264,0.0004040597,0.000368346,0.0002249205,0.0004019532,0.0006727775,0.0001208334,0.00001057305],"category_scores_gemma":[0.0002920968,0.0001445141,0.0002878624,0.0003267991,0.0000335018,0.0009142351,0.0001055814,0.0002921812,5.853358e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007605249,"about_ca_system_score_gemma":0.0002374744,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003942772,"about_ca_topic_score_gemma":0.000002747337,"domain_scores_codex":[0.9979814,0.0004161806,0.0006656788,0.0002752963,0.0003738985,0.0002875433],"domain_scores_gemma":[0.9985426,0.0001511689,0.0004268196,0.0004423634,0.0002950357,0.0001419849],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001072283,0.0005784611,0.0001278458,0.0002426993,0.0002718975,0.00004976231,0.001406928,0.0008404989,0.142413,0.01225302,0.1170783,0.7236653],"study_design_scores_gemma":[0.01668335,0.002396251,0.1365414,0.0007211157,0.0005335696,0.001563901,0.0007357407,0.4673257,0.1282866,0.1352283,0.1077616,0.002222461],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00981218,0.0004908409,0.9832777,0.001391699,0.001267939,0.0002020791,0.000002119852,0.00003635586,0.00351906],"genre_scores_gemma":[0.1080077,0.00003987159,0.8881393,0.001608597,0.0008235183,0.000009501607,0.000006266201,0.00001678372,0.00134849],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7214428,"threshold_uncertainty_score":0.5893113,"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."}}