{"id":"W6949870821","doi":"10.5281/zenodo.3731032","title":"Cryptocurrencies and the future of money. Money and trust in Mexico","year":2020,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Crime, Illicit Activities, and Governance","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Hyperinflation; Currency; Inflation (cosmology); Latin Americans; Cryptocurrency; Legal tender; Circulation (fluid dynamics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"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":[0.0008359608,0.0001314063,0.0001798024,0.001021535,0.004514612,0.005472938,0.0003280497,0.001519259,0.008909017],"category_scores_gemma":[0.002023627,0.00009003589,0.0001210981,0.001470725,0.007844425,0.004597454,0.001826618,0.002139453,0.0002288311],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01025571,"about_ca_system_score_gemma":0.002382298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03507835,"about_ca_topic_score_gemma":0.03669335,"domain_scores_codex":[0.9996183,0.0001195036,0.00001061823,0.00004986072,0.00005738008,0.0001442249],"domain_scores_gemma":[0.9989842,0.0002968731,0.0003595075,0.00005427601,0.00009795263,0.0002071461],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.00002369583,0.000005827178,0.001404199,0.00002231237,0.000002551629,0.0001426746,0.002361345,0.00006144367,0.0000203055,0.9810043,0.00529018,0.009661129],"study_design_scores_gemma":[0.00004249473,0.00004882589,0.02445525,0.0006314139,0.0000216227,0.0007582552,0.01239271,0.00103853,0.0001891682,0.5089375,0.4514481,0.00003609853],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1859305,0.08052006,0.002245991,0.1435016,0.0007234851,0.0000306147,0.0002743448,0.0000381862,0.5867352],"genre_scores_gemma":[0.9763319,0.009817958,0.0002383786,0.0009616453,0.0003609007,0.00001169907,0.0000255396,0.0000057999,0.01224614],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03507835,"threshold_uncertainty_score":0.07441074,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03110090542853021,"score_gpt":0.2550507714582017,"score_spread":0.2239498660296715,"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."}}