Bibliographic record
Abstract
To personally stay abreast of current water issues, I try to read and participate in water dialogues when I can take advantage of handy opportunities. In the past year, what I've heard and read has changed how I think about water even though it is so much a daily part of everyone's life that it can become rather mundane. At the international SWCS conference in Rochester this past summer, we heard Sandra Postel, author of a number of books and most recently, Liquid Assets, very eloquently describe the growing worldwide water demand, potential impacts to humans, and the need to provide for flows that sustain stream and river functions. Just a few hours ago, I listened to a Canadian water consultant by the name of Patrick Lucey state that, “The future will be written in water, not ink.” According to Lucey, Canada has 25 percent of the surface freshwater supplies in the world. Another 25 percent is contained within Lake Baikal in Russian Siberia. That leaves 50 percent of all freshwater for the rest of the world to share. In this future high stakes game there undoubtedly will be clear winners and losers. China …
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.050 | 0.009 |
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".