Institutional repository of CSIR-NML and the global information seeker
Bibliographic record
Abstract
CSIR-National Metallurgical Laboratory (NML) Jamshedpur established its institutional repository – Eprints@NML inSeptember 2009. The study looks at the use of the repository based on the repository log data. It was found that NMLScientists received 1847 enquires for their articles/projects until September, 2012 which motivated them further to enrich therepository by uploading their research outputs. As a result, by the end of September 2012 there were 5071 uploads as against3972 documents uploaded in December 2011. A total of 27, 40,343 hits were received from different countries duringAugust 2011 to September 2012 and a cumulative total of over 4.86 million hits since inception. The maximum number ofhits was 0.27 million in August, 2012. More than 75% of NML scientists/researchers have registered with Eprints@NMLfor uploading their documents. The top twenty countries accessing the repository were United States, India, Russia, China,UK, Hong Kong, Germany, Netherlands, Iran, Japan, France, Italy, Canada, Korea, Ukraine, Brazil, Poland, Australia,Turkey and South Africa.
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 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.478 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".