{"id":"W2560965341","doi":"10.5539/ibr.v10n2p9","title":"The Determinants of Credit Growth in Lebanon","year":2016,"lang":"en","type":"article","venue":"International Business Research","topic":"Banking stability, regulation, efficiency","field":"Economics, Econometrics and Finance","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Remittance; Inflation (cosmology); Loan; Monetary economics; Economics; Money supply; Lag; Interest rate; Distributed lag; Private sector; Panel data; Credit risk; Order (exchange); Financial system; Business; Finance; Econometrics","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":[0.002241484,0.00006274775,0.000136198,0.0004260228,0.00008642228,0.00005330622,0.0006660973,0.00005316771,0.0002560209],"category_scores_gemma":[0.003202447,0.00004396195,0.00003457237,0.0006280991,0.0003146913,0.0002322975,0.0001572513,0.00008889309,0.000150522],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002046781,"about_ca_system_score_gemma":0.00005708825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005594664,"about_ca_topic_score_gemma":0.0002576333,"domain_scores_codex":[0.9987336,0.00003980245,0.0005119846,0.0002595322,0.000204127,0.0002509833],"domain_scores_gemma":[0.9982581,0.0006656621,0.0001434606,0.0002499112,0.0006614582,0.00002145981],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000305973,0.00007771159,0.9126576,0.00001152557,0.000005440066,0.000001638907,0.00006345916,0.000008757422,0.0001149151,0.08129527,0.0001862876,0.00554678],"study_design_scores_gemma":[0.0002861664,0.00001270367,0.9017082,0.00004271601,1.838786e-7,0.000001524057,0.00000857894,0.000449731,0.000529453,0.09222557,0.004679887,0.00005524248],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9838908,0.0003113995,0.0006433369,0.004375253,0.0006202402,0.0001533134,0.00004931377,0.000008443022,0.009947857],"genre_scores_gemma":[0.9982812,0.0002463564,0.00005732775,0.000005582423,0.0001085152,0.0000260553,0.000001715581,0.00001014228,0.001263058],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0143904,"threshold_uncertainty_score":0.383386,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06803052297671439,"score_gpt":0.3322299269044618,"score_spread":0.2641994039277474,"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."}}