{"id":"W2514560096","doi":"10.1016/j.jcomm.2016.07.004","title":"Natural gas storage valuation, optimization, market and credit risk management","year":2016,"lang":"en","type":"article","venue":"Journal of commodity markets","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"","keywords":"Valuation (finance); Hedge; Volatility (finance); Markov process; Valuation of options; Mathematical optimization; Econometrics; Computer science; Partial differential equation; Risk management; Economics; Mathematics; Finance; Statistics","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":[0.0008171292,0.0003657111,0.0006203478,0.0004142833,0.0002118282,0.002264841,0.0004005691,0.0008490487,0.001748803],"category_scores_gemma":[0.004267876,0.0003000529,0.0003282461,0.0005652143,0.0007543266,0.002293689,0.0003580789,0.000550386,0.00005908434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001130475,"about_ca_system_score_gemma":0.001080155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007193052,"about_ca_topic_score_gemma":0.005588289,"domain_scores_codex":[0.9998134,0.00008115303,0.00001368352,0.000036797,0.00003474619,0.00002019458],"domain_scores_gemma":[0.9990551,0.0006048267,0.0001567429,0.00004324532,0.00009849016,0.00004156451],"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":[0.0001153699,0.00003204461,0.00425282,0.000035513,0.00003803962,0.00009623413,0.00002354355,0.9392728,0.001195113,0.04430771,0.0007030276,0.009927791],"study_design_scores_gemma":[0.00000619502,0.00001385017,0.001091467,0.000003872938,0.000009128062,0.00002238982,0.00001913562,0.9742276,0.0003638076,0.02395502,0.0002785012,0.000009098945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7143335,0.002997941,0.2629766,0.002832443,0.0001182806,0.00006092625,0.0005151746,0.0001425742,0.0160227],"genre_scores_gemma":[0.9947977,0.0003172901,0.002785843,0.00001026728,0.00002665533,0.000004862691,0.00004470761,0.00001000544,0.00200263],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007193052,"threshold_uncertainty_score":0.01430237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0144391909055022,"score_gpt":0.2137109738438132,"score_spread":0.199271782938311,"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."}}