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 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 | 0.018 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.023 | 0.053 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.159 | 0.086 |
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".