Bibliographic record
Abstract
This type of experiment is not the first time in the banking sector of India, many times before it has happened. For reforms in the Indian banking sector, merger was done in 1991 to create a three-level structure with 10 national banks in 1991, in which about 49 have been merged. With the State Bank of India five associate banks and Indian women bank can be called the biggest merger of 2017. Because keeping in mind the quick action and changing backing pattern, it is probably the decision of the State Bank of India. State Bank of India does not have any similarity with other commercial banks, but merger strengthens its strategy. State Bank of India is known as India's largest lender bank. In the month of April, the Indian women bank and five other banks were included in it. The result of this will be the result of what will happen in the future. In the coming quarter, it may be that the expected result is not seen, but the merger has always been a good result, and everyone is aware. The modern competition period, which has not only brought national status to the State but also the State Bank of India on the national level? With the help of merger, State Bank of India can achieve its place in the top 50 banks in the world.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".