{"id":"W7132946526","doi":"","title":"A Look at Model Uncertainty in the Evaluation of Commodity Contingent Claims: A Practitioner's Guide","year":2013,"lang":"en","type":"dissertation","venue":"TSpace","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto","keywords":"Flexibility (engineering); Measure (data warehouse); Commodity; Characterization (materials science); Key (lock); Financial market; Model risk; Uncertainty analysis","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0097791,0.0002611065,0.0004795959,0.0003652522,0.0001834364,0.0001659845,0.0007469531,0.0003159509,0.0007895853],"category_scores_gemma":[0.00283108,0.0001740266,0.0001754317,0.0007048909,0.00006403214,0.0003091294,0.00005092011,0.0003165376,0.0001384855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002811407,"about_ca_system_score_gemma":0.0006351581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002084398,"about_ca_topic_score_gemma":0.009055301,"domain_scores_codex":[0.9934128,0.001107747,0.001118047,0.0005209994,0.003596894,0.0002435087],"domain_scores_gemma":[0.9941385,0.0008904124,0.001656095,0.0009587441,0.002296985,0.0000592858],"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":[0.0002012497,0.0002193003,0.0006831928,0.00002227127,0.00003593461,0.000001725934,0.03845029,0.7666971,0.0003609887,0.001408828,0.1751627,0.01675643],"study_design_scores_gemma":[0.0007836685,0.00005131027,0.004729585,0.00009723937,0.0001731842,0.000003472121,0.02002187,0.9485456,0.000317249,0.0146701,0.01032979,0.000276918],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9168382,0.0005853986,0.001167007,0.0008816021,0.0004618679,0.001752319,0.00004340547,0.00002249235,0.07824766],"genre_scores_gemma":[0.9542353,0.0006766825,0.0009628569,0.000222127,0.0000971601,0.0003480245,0.001822477,0.00002998481,0.04160544],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1818485,"threshold_uncertainty_score":0.8645406,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2209379823049817,"score_gpt":0.5043900127806263,"score_spread":0.2834520304756447,"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."}}