{"id":"W3208740342","doi":"10.3390/jrfm14110516","title":"Crypto Exchanges and Credit Risk: Modeling and Forecasting the Probability of Closure","year":2021,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Russian Science Foundation","keywords":"Quarter (Canadian coin); Sample (material); Cryptocurrency; Computer science; Closure (psychology); Covariate; Econometrics; Actuarial science; Business; Computer security; Economics; Machine learning","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003483562,0.000578229,0.0007158711,0.001650538,0.0004958072,0.001830395,0.0012958,0.001893697,0.005794673],"category_scores_gemma":[0.01159361,0.0002937927,0.0007662164,0.001570975,0.0007212328,0.001839118,0.0008307504,0.002237237,0.0008069644],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001057135,"about_ca_system_score_gemma":0.0007932436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01659425,"about_ca_topic_score_gemma":0.01195975,"domain_scores_codex":[0.999447,0.0002046769,0.00003736434,0.0001360499,0.00004958324,0.0001252043],"domain_scores_gemma":[0.9893221,0.007234241,0.002084263,0.0004535285,0.0003237736,0.0005821642],"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":[0.0006561792,0.0005169064,0.458661,0.00008276577,0.0001332045,0.0003799925,0.0002051722,0.4981679,0.0002917876,0.01100526,0.005040718,0.02485924],"study_design_scores_gemma":[0.00004176824,0.000088291,0.02846224,0.00002857161,0.00002381336,0.00006555338,0.0001198644,0.9632536,0.0001291997,0.006979486,0.0007906066,0.00001702132],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9834553,0.0005121683,0.01097676,0.001301242,0.00003826846,0.00005650007,0.002178635,0.00008767776,0.001393521],"genre_scores_gemma":[0.9941659,0.0002800174,0.002007545,0.00003868722,0.0000549264,0.00004293105,0.001960448,0.000008956745,0.001440691],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01659425,"threshold_uncertainty_score":0.03299534,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01539933640163108,"score_gpt":0.2102068318344143,"score_spread":0.1948074954327833,"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."}}