{"id":"W4226023275","doi":"10.5267/j.ijdns.2022.4.011","title":"Cryptocurrencies: A bibliometric analysis","year":2022,"lang":"en","type":"article","venue":"International Journal of Data and Network Science","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cryptocurrency; Scopus; Currency; Web of science; Bibliometrics; Content analysis; Data science; China; Citation analysis; Computer science; Library science; Political science; World Wide Web; Social science; Sociology; Citation; Economics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["bibliometrics","open_science"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.002582859,0.00005212217,0.0001186046,0.01934405,0.0003618666,0.000224458,0.007587208,0.00001337856,0.00003268476],"category_scores_gemma":[0.00009742532,0.00004594032,0.00003705556,0.09511897,0.0002105332,0.001061046,0.003675984,0.0002159242,9.260133e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004535332,"about_ca_system_score_gemma":0.0001808341,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007179617,"about_ca_topic_score_gemma":0.000002608755,"domain_scores_codex":[0.9983519,0.00003181365,0.0002684834,0.0002650449,0.0009279342,0.0001548467],"domain_scores_gemma":[0.9986426,0.0001132326,0.0002961039,0.0005414116,0.0003345698,0.00007208987],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001896175,0.0002613818,0.04146448,0.000001716404,0.0004865107,0.00006510839,0.0004211075,0.005243135,0.0002309844,0.2028479,0.01257367,0.736385],"study_design_scores_gemma":[0.0005245307,0.000234756,0.07388117,0.000008615635,0.0001106713,0.0007203894,0.0001769219,0.7141805,0.00008324901,0.04318674,0.1666301,0.0002624452],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1366641,0.001820997,0.8567021,0.003609491,0.000858016,0.0000507534,0.00004359418,0.00002721033,0.0002237103],"genre_scores_gemma":[0.9621016,0.0002685478,0.03727792,0.0002483737,0.00008920221,0.000002554385,0.00000432664,0.000001133302,0.000006325365],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8254375,"threshold_uncertainty_score":0.9977822,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03059741994411757,"score_gpt":0.3206519238158543,"score_spread":0.2900545038717367,"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."}}