{"id":"W2270692170","doi":"","title":"Go green - a case study of green banking initiatives of ICICI Bank Limited","year":2015,"lang":"en","type":"article","venue":"International Conference on Bioinformatics","topic":"Environmental Sustainability in Business","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Subsidiary; Business; Finance; China; Retail banking; Multinational corporation; Geography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001830683,0.0004030387,0.0002943882,0.002148233,0.01219014,0.006202682,0.001462519,0.002451831,0.005041942],"category_scores_gemma":[0.003440678,0.000322665,0.0002827087,0.003688899,0.005432273,0.00306792,0.005075616,0.003222647,0.0006812052],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006468247,"about_ca_system_score_gemma":0.005236297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02334057,"about_ca_topic_score_gemma":0.06877063,"domain_scores_codex":[0.9971858,0.00135583,0.00005696729,0.0001752076,0.0004150051,0.0008112185],"domain_scores_gemma":[0.9949716,0.001720244,0.0007751769,0.000259808,0.000463072,0.001810066],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0002560776,0.00100594,0.0971785,0.0004905424,0.00004451396,0.07564753,0.6818517,0.001069772,0.00305883,0.06221619,0.02766031,0.04952005],"study_design_scores_gemma":[0.000009871234,0.0001446281,0.03337517,0.0003680649,0.00002210366,0.006000368,0.8500847,0.0007275234,0.0007009547,0.002020734,0.1064995,0.00004631378],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9363855,0.0007222805,0.0006754775,0.005068764,0.000046727,0.00008986616,0.00007597361,0.00003924996,0.05689608],"genre_scores_gemma":[0.9866813,0.0008549223,0.0006115664,0.001235659,0.0000210016,0.00005215107,0.00004319272,0.00002384985,0.01047626],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02334057,"threshold_uncertainty_score":0.04693067,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07529625302438155,"score_gpt":0.2945318788093184,"score_spread":0.2192356257849368,"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."}}