Funding Opportunities for Research and Graduate Education: How the NIH Can Help You
Bibliographic record
Abstract
The National Institutes of Health (NIH) is the world’s foremost medical research center and largest funding source of biomedical research. Its mission is science in pursuit of fundamental and creative discoveries as they relate to extending healthy lives and preventing illness, as well as knowledge about living systems and the application of that knowledge to help prevent, detect, diagnose, and treat disease and disability in human beings. Through its extramural program, the NIH supports some 46,700 grants at universities, medical schools, and other research institutions in the United States and elsewhere. However, NIH-funded biomedical researchers at veterinary schools and colleges are underrepresented in contributing to the growing knowledge that the federally provided NIH funds make possible. This article outlines possible approaches to NIH funding for academic research scientists located at veterinary institutions; we hope to either introduce them to or expand their knowledge of and insight into NIH funding.
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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 | 0.058 | 0.133 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.028 | 0.027 |
| Open science | 0.005 | 0.028 |
| Research integrity | 0.027 | 0.025 |
| Insufficient payload (model declined to judge) | 0.055 | 0.037 |
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".