{"id":"W4386324359","doi":"10.3390/jrfm16090390","title":"Impact of Liquidity on the Efficiency of Banks in India Using Panel Data Analysis","year":2023,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Market liquidity; Panel data; Transparency (behavior); Econometrics; Cost efficiency; Returns to scale; Business; Scale (ratio); Economics; Monetary economics; Financial system; Computer science; Production (economics); Microeconomics; Geography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002398294,0.0002026337,0.0003314893,0.001427502,0.000209275,0.001981736,0.0003922302,0.0004885664,0.001665328],"category_scores_gemma":[0.008482079,0.0001654178,0.0008075563,0.002050024,0.0004281825,0.0008842226,0.0008200829,0.0008346973,0.0003398469],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009526436,"about_ca_system_score_gemma":0.0006216677,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01535592,"about_ca_topic_score_gemma":0.01109765,"domain_scores_codex":[0.998418,0.0006027067,0.0001597186,0.0002421676,0.0002832515,0.0002941647],"domain_scores_gemma":[0.9847438,0.008006764,0.004805,0.0008577394,0.001261137,0.0003255922],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002641921,0.00009364157,0.9560204,0.00009980392,0.000526235,0.0003060587,0.0003378991,0.02523854,0.0007596387,0.001554766,0.001202187,0.01359657],"study_design_scores_gemma":[0.00001259039,0.0001534309,0.9690114,0.0000368587,0.0002889321,0.0001666149,0.0006104627,0.02579703,0.001443275,0.000814711,0.001623238,0.00004143641],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9936675,0.0005102788,0.001211525,0.0004335496,0.000007558683,0.00001140303,0.001642219,0.00003153906,0.00248446],"genre_scores_gemma":[0.9988525,0.0001004364,0.0001511939,0.00002239552,0.000007699854,0.000003615504,0.0005734108,0.000002870078,0.0002859014],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01535592,"threshold_uncertainty_score":0.03053308,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1281754739112934,"score_gpt":0.3915966066455018,"score_spread":0.2634211327342085,"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."}}