{"id":"W4401027727","doi":"10.1016/j.energy.2024.132560","title":"What drives the high-risk spillover of benchmark oil prices into China's LNG market?","year":2024,"lang":"en","type":"article","venue":"Energy","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"Natural Science Basic Research Program of Shaanxi Province; National University's Basic Research Foundation of China; Xidian University; National Natural Science Foundation of China","keywords":"Spillover effect; China; Benchmark (surveying); Business; Economics; Natural resource economics; Environmental science; Microeconomics","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":[0.0004337388,0.0002134058,0.0003988456,0.0007087387,0.0004233154,0.002247674,0.0003886718,0.0009203224,0.004784511],"category_scores_gemma":[0.002058991,0.000228099,0.0003476612,0.0005566417,0.0006764028,0.002054389,0.0006541774,0.0007199435,0.0003562593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001272107,"about_ca_system_score_gemma":0.001041288,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01511471,"about_ca_topic_score_gemma":0.01539118,"domain_scores_codex":[0.9998481,0.00001862617,0.000009011925,0.00003636707,0.00003021049,0.00005763258],"domain_scores_gemma":[0.9992901,0.0001614138,0.0002908523,0.00003538629,0.0001147323,0.000107469],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006711397,0.0004832927,0.7555603,0.0002969262,0.0004564614,0.002854051,0.001785824,0.03186284,0.01590789,0.1288412,0.01035836,0.05092169],"study_design_scores_gemma":[0.0001002009,0.0001545831,0.8607366,0.00006573616,0.0002312902,0.0003023942,0.00318445,0.08080335,0.004092319,0.04337014,0.006846832,0.0001121326],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.98737,0.0004340398,0.0008655475,0.001967257,0.0000345658,0.00001190436,0.0001548697,0.00003536931,0.009126429],"genre_scores_gemma":[0.9990135,0.0001887715,0.00005466577,0.00007329838,0.0000241319,0.000001030068,0.00004102907,0.000003886572,0.000599567],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01511471,"threshold_uncertainty_score":0.03005344,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005420752940713747,"score_gpt":0.1917270887564365,"score_spread":0.1863063358157227,"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."}}