{"id":"W4362670129","doi":"10.54097/hbem.v5i.5084","title":"Cryptocurrency under Local Conflict: Evidence from Soaring Crude Oil Price","year":2023,"lang":"en","type":"article","venue":"Highlights in Business Economics and Management","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Cryptocurrency; Sanctions; Shock (circulatory); Crude oil; Yield (engineering); Damages; Economics; Brent Crude; Monetary economics; Economy; Business; Oil price; Financial economics; Political science; Engineering; Computer security; Petroleum engineering; Computer science; Law","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006729494,0.0002705787,0.0004950252,0.0004743332,0.0001374625,0.0002121237,0.0003492678,0.0001217005,0.0001850308],"category_scores_gemma":[0.00002294152,0.0003114419,0.00006468,0.0004914537,0.00009879873,0.0004259681,0.000459404,0.0001027173,0.0001889104],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002026861,"about_ca_system_score_gemma":0.00001690838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00147386,"about_ca_topic_score_gemma":0.0006140076,"domain_scores_codex":[0.9978088,0.00001579289,0.0008150727,0.0008688874,0.00003802505,0.0004534133],"domain_scores_gemma":[0.9989421,0.0001220937,0.0002739057,0.0005282635,0.00002999075,0.0001036776],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00003586464,0.0000690272,0.009232259,0.0002164081,0.00007994186,0.00001676772,0.0002127862,0.002026021,0.000001847193,0.9818244,0.0003682348,0.005916471],"study_design_scores_gemma":[0.0008564303,0.00001240384,0.4681106,0.0001721326,0.00001362297,8.769724e-7,0.00008715288,0.1531922,0.000005677227,0.04174094,0.3352553,0.0005527086],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9102048,0.003192814,0.0230041,0.01449706,0.002242498,0.0005175796,0.0001738849,0.0001813134,0.04598595],"genre_scores_gemma":[0.7901345,0.2057488,0.0008866368,0.0002792954,0.0001284105,0.0001247348,0.00007128603,0.00004728776,0.002578965],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9400834,"threshold_uncertainty_score":0.9999338,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04480419770366072,"score_gpt":0.234323968057169,"score_spread":0.1895197703535083,"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."}}