Indian Research Information Network System (IRINS): Analysis of University of Delhi Faculty Profiles in IRINS
Bibliographic record
Abstract
This study main examine the Role of Indian Research Information Network System (IRINS) Analysis of University of Delhi Faculty Profiles in IRINS. The study explores the resource impact from scholarly resources, Subject Wise, Designation Wise, Gender Wise, Top Ten Profiles, Total Publication of University, Types of Publication, Categories of Publication and H-Index. Social science subject is highest number in IRINS. Engineering and technology is a second highest in this table, faculty of Delhi University , Agricultural science with 10 faculty profile in IRINS, the Chemical Sciences (8) is a lowest in this table. Professor Designation With (91) maximum Profile of Faculty in INIRS .The male (119) faculty profile number is more than to female (51) faculty number. Prof. Brajesh Chandra Choudhary is a highest publication with 1620. Research Output the total publication with 7571 and Patents with 6 Closed Access with 2812 Type of publication in IRINS, second is Gold Open access with 808, Green OA with 542, Bronza open access with 199. Articles publication by faculty member of Delhi university, with 5724”
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.026 | 0.076 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".