{"id":"W4395083876","doi":"10.1186/s40854-024-00628-0","title":"Assessing efficiency in prices and trading volumes of cryptocurrencies before and during the COVID-19 pandemic with fractal, chaos, and randomness: evidence from a large dataset","year":2024,"lang":"en","type":"article","venue":"Financial Innovation","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Cryptocurrency; Coronavirus disease 2019 (COVID-19); Pandemic; Fractal; Randomness; CHAOS (operating system); 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Hurst exponent; Econometrics; Statistical physics; Economics; Mathematics; Computer science; Statistics; Physics; Medicine; Computer security; Virology; Mathematical analysis; Outbreak","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":[0.0008051076,0.0001018163,0.000287464,0.0003048264,0.0001772115,0.0002452893,0.00006719359,0.00004827658,0.00002459499],"category_scores_gemma":[0.0003737303,0.00008088967,0.00001355101,0.0009576032,0.00008810073,0.0007693141,0.00005735741,0.0001116782,5.883037e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004210834,"about_ca_system_score_gemma":0.00004392931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001920278,"about_ca_topic_score_gemma":0.00184169,"domain_scores_codex":[0.9990535,0.00001433423,0.0004635018,0.0002973488,0.00004360224,0.0001276763],"domain_scores_gemma":[0.99944,0.0001579989,0.0002446434,0.0001139223,0.00002422009,0.00001926915],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004860551,0.00001870501,0.9712118,0.0005086208,0.00002749659,0.000004669335,0.003705506,0.00001543787,0.0002479513,0.0215117,0.00009101196,0.00260848],"study_design_scores_gemma":[0.0008010371,0.00005973632,0.9579388,0.0004283226,0.00001952974,0.00001309876,0.0005771138,0.02547224,0.00001411156,0.00591786,0.00856977,0.0001883624],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9822577,0.00953419,0.007121408,0.0002463055,0.00005417687,0.0001723982,0.0005881725,0.0000127574,0.00001284133],"genre_scores_gemma":[0.9993871,0.0002824033,0.000119584,0.00003911063,0.00005298581,0.00001813142,0.00008512425,0.000006502351,0.000009061833],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0254568,"threshold_uncertainty_score":0.3298585,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05995961367129506,"score_gpt":0.2920452904102689,"score_spread":0.2320856767389739,"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."}}