{"id":"W3161950946","doi":"","title":"The Effect of Non-cash Transactions on The Money Supply Indonesia (2009:Q1 – 2019:Q2)","year":2020,"lang":"en","type":"article","venue":"UAJY Repository (University of Southampton)","topic":"Islamic Finance and Banking Studies","field":"Business, Management and Accounting","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"ATM card; Cash; Database transaction; Credit card; Electronic money; Business; Volume (thermodynamics); Money supply; Debit card; Credit card interest; Financial transaction; Quarter (Canadian coin); Commerce; Monetary economics; Payment; Economics; Finance; Database; Computer science; Interest rate","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001956481,0.0001545422,0.0002493065,0.0000535539,0.0009519422,0.00004386567,0.0004170537,0.00006550305,0.00003621763],"category_scores_gemma":[0.00001474587,0.000108281,0.0001966476,0.0003328476,0.0002689835,0.0002628744,0.00005722253,0.0001881053,0.00007694622],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001515138,"about_ca_system_score_gemma":0.00001662893,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006800336,"about_ca_topic_score_gemma":0.00003609963,"domain_scores_codex":[0.9991844,0.0000272182,0.0001432144,0.0002053991,0.0002506794,0.0001890732],"domain_scores_gemma":[0.9992213,0.0001663696,0.0002614144,0.0002537863,0.00009107858,0.00000605885],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.007276996,0.00071228,0.5505314,0.002633856,0.003403697,0.000435909,0.03305309,0.002424739,0.04189471,0.01180853,0.2144679,0.1313569],"study_design_scores_gemma":[0.006921952,0.001361566,0.8924274,0.0009568077,0.002096991,0.00001592645,0.01795748,0.007716314,0.0212813,0.0005829983,0.04704395,0.001637239],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9920217,0.0001020615,0.0006439512,0.00229788,0.0002625476,0.0002878124,0.000008383415,0.00004529069,0.004330413],"genre_scores_gemma":[0.9986889,0.00002518035,0.00001385272,0.0001513618,0.0001558898,0.000001054599,0.000003009655,0.00001102746,0.0009497309],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3418961,"threshold_uncertainty_score":0.7321666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005062473518764827,"score_gpt":0.157255516051183,"score_spread":0.1521930425324182,"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."}}