1 Analysis of Non-Performing Assets of Tamil Nadu based
Why this work is in the frame
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Bibliographic record
Abstract
As far as the present scenario is concerned the banking industry in India is in a transition phase. The Public Sector Banks, which are the foundation of the Indian Banking system account for more than 78 per cent of total banking industry assets. Unfortunately, they are burdened with excessive Non-Performing assets (NPAs), massive manpower and lack of modern technology. During FY12, asset quality of banks was severely impaired, as revealed by the steep increase in non-performing assets of Scheduled Commercial Banks, particularly for public sector banks owing to their significant exposure to troubled sectors such as power, aviation, real estate and telecom. There was a significant increase noted in the NPA levels during FY12. Gross NPAs value recorded a y-o-y growth of 45.3 % and net NPAs registered a y-o-y growth of 55.6 % during FY12. As per RBI, this increase was due to inadequate credit appraisal process coupled with unfavorable economic situation in the domestic as well as foreign market. Private Sector Banks have maintained its asset quality. GNPA of Private Sector Banks marginally decreased by 1bps to 2.19 % or ~ INR21246 crores in quarter ended December 2012 as against 2.20 % or~INR20884 crores in the quarter ended September 2012. The PvtSCBs are classified as old (13) and new (07) private sector banks. Among the thirteen Old PvtSCBs in India, only four banks are having their registered office in
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it