{"id":"W3160327816","doi":"10.1142/s0218348x21501851","title":"MULTI-SCALE ANALYSIS REVEALS DIFFERENT PATTERNS IN TECHNICAL INDICATORS OF BLOCKCHAIN","year":2021,"lang":"en","type":"article","venue":"Fractals","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University; Concordia University","funders":"","keywords":"Blockchain; Distributed ledger; Scale (ratio); Asset (computer security); Computer science; Cryptocurrency; Technical analysis; Hurst exponent; Work (physics); Computer security; Business; Mathematics; Statistics; Engineering; Finance","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003631996,0.0001431625,0.001049166,0.000823261,0.00003293817,0.00002808429,0.000190514,0.0001174572,0.002403358],"category_scores_gemma":[0.00008255697,0.000152685,0.0004492065,0.001646389,0.0000293177,0.00003940727,0.0001210256,0.0001341579,0.00004619218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007892068,"about_ca_system_score_gemma":0.000009530414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008849371,"about_ca_topic_score_gemma":0.00233363,"domain_scores_codex":[0.9981735,0.00003910605,0.001070785,0.0004221735,0.00006187829,0.0002324939],"domain_scores_gemma":[0.9988043,0.0000700794,0.0004908537,0.0005272751,0.00002894089,0.00007854598],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000003143054,0.0002522728,0.9969691,0.00003535548,0.0003080087,0.00001010827,0.0001375756,0.0001434466,0.0007366374,0.001109745,0.0000383502,0.000256236],"study_design_scores_gemma":[0.00029861,0.00001700406,0.9952306,0.00002338207,0.00006959208,0.000001326129,0.0001267694,0.002070107,0.0003456734,0.0004962778,0.001140525,0.000180126],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9921454,0.00109885,0.004569188,0.0001648059,0.00005727246,0.00009494664,0.0003339816,0.00001513138,0.001520472],"genre_scores_gemma":[0.9978114,0.00006917441,0.0007563249,0.00005548186,0.00003003021,0.00001997268,0.00005935794,0.00001397546,0.001184273],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005666058,"threshold_uncertainty_score":0.9985086,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01962573827177808,"score_gpt":0.234946414917899,"score_spread":0.2153206766461209,"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."}}