Bibliographic record
Abstract
I am pleased to report on the release of the National Institutes for Water Resources (NIWR) Special Issue in the Journal of Contemporary Water Research & Education (JCWRE) that features important water research by researchers and students studying at our collective institutions of higher learning. This JCWRE Special Issue is a partnership between NIWR, which consists of the 54 land-grant university water institutes in the United States, and UCOWR, which represents 63 of the best water research universities in the United States and Canada. This timely water research is supported by Sec. 104b and 104g grants from the Department of Interior and U.S. Geological Survey appropriated by Congress through the 1964 Water Resources Research Act as amended in 1988. This peer-reviewed research includes articles on water quantity and quality from universities that stretch from east to west and from coast to coast that focus on most of the large river basins and watersheds in America. I wish to especially thank Jackie Gillespie and Karl Williard, Co-editors of JCWRE, for pushing this collaboration forward. Upon rereading the articles published in this Special Issue of JCWRE, I am reminded that the future of our field is in good hands to tackle the critical water resources issues of the day as they appear more and more in the headlines and front pages of the news. Warmly,
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.029 | 0.028 |
| Insufficient payload (model declined to judge) | 0.017 | 0.017 |
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".