{"id":"W2012468412","doi":"10.5539/ijef.v2n3p170","title":"Predicting Future Depositor`s Rate of Return Applying Neural Network: A Case-study of Indonesian Islamic Bank","year":2010,"lang":"en","type":"article","venue":"International Journal of Economics and Finance","topic":"Financial Distress and Bankruptcy Prediction","field":"Business, Management and Accounting","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Exchange rate; Circulation (fluid dynamics); Econometrics; Artificial neural network; Certificate; Inflation (cosmology); Indonesian; Interest rate; Currency; Computer science; Economics; Finance; Monetary economics; Artificial intelligence; Algorithm; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0002841404,0.00009844625,0.0002197849,0.0001278863,0.00007307397,0.0000829406,0.0002000449,0.00005963672,0.000004387666],"category_scores_gemma":[0.00002275749,0.00009259272,0.00006964673,0.00007088244,0.00003557275,0.0006153933,0.00008092289,0.000218132,2.26929e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001069891,"about_ca_system_score_gemma":0.0000187366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000657321,"about_ca_topic_score_gemma":0.001196243,"domain_scores_codex":[0.9991447,0.000006101558,0.0005691524,0.0001133491,0.00006502528,0.0001016148],"domain_scores_gemma":[0.9984813,0.0000256287,0.001143441,0.00007752525,0.0002668815,0.00000527542],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006567648,0.0005246347,0.8437045,0.0001030649,0.0003003835,0.0006358477,0.0006275362,0.03564294,0.001421702,0.04237863,0.0002281811,0.07377581],"study_design_scores_gemma":[0.005087349,0.0003163357,0.6238516,0.000308906,0.0002170375,0.001855675,0.002777915,0.3510714,0.0002519233,0.007529889,0.0062445,0.0004874041],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964752,0.00009437324,0.00003759331,0.0001451669,0.002929958,0.0001034053,0.000007050724,0.000003278967,0.0002039564],"genre_scores_gemma":[0.9960895,0.0001366941,0.0001076266,0.00005970698,0.003586124,0.000003289574,0.000002585811,0.000009755853,0.000004706146],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3154285,"threshold_uncertainty_score":0.3775821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007962606990086638,"score_gpt":0.2039961073787668,"score_spread":0.1960335003886801,"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."}}