Report on working group deliberations, water quality group, Second regional workshop on integrated monitoring
Bibliographic record
Abstract
Environmental monitoring data on water quality and aquatic biota are necessary to evaluate the spatial distribution and temporal trends of pollutants, identify their sources, and evaluate effects. In addition, an efficient method of making data readily available to users is needed. For the transboundary region near the border between the Province of Quebec and the States of New York and Vermont, lake acidification is a prominent concern that poses difficult questions with regard to long-term trends, the relative importance of anthropogenic and natural sources of change, and methods of remediation. Other recurring concerns include the effects of municipal, agricultural, and industrial sources of contamination on the quality of drinking water, on the uses of surface waters for recreational purposes, and on the preservation of ecosystems. For the purposes of both research and regulation, background levels of pollutants and the natural state of water quality ideally should be determined. In this transboundary region as in others, however, changes in aquatic environments often occur before adequate monitoring begins.
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 | 0.013 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.008 | 0.003 |
| Insufficient payload (model declined to judge) | 0.076 | 0.039 |
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".