Updating Water Quality Standards Criteria Considering Chemical Mixtures in the Context of Climate Change
Bibliographic record
Abstract
Human activity has rapidly impacted the world; however, regulations have not kept pace to protect human life and the environment. Chemical pollution and climate change are consequences of the accelerated development that have not been sufficiently incorporated in regulations regarding water quality. This paper explores chemical pollution and climate change as criteria for water quality regulation updates, and it examines global north–south relations using a thorough literature review including papers and relevant regulations regarding surface water standards in different countries and proposes ways forward for the field of water quality. Water Quality Standards (WQS) definitions are defined by regulatory bodies that primarily consider toxicological assays provided by companies or literature-based research on emerging compounds, primarily conducted in laboratory conditions that differ from realistic environments, where compounds may be co-exposed to other contaminants and under variable temperatures. The research provided evidence that discussions on updating WQS to account for chemical mixtures are advanced in some countries such as the Netherlands, but implementation remains necessary. Furthermore, updates in WQS regarding climate change focus mostly on avoiding the climate crisis by reducing emissions. However, updates are not implemented rapidly enough to enhance protection under realistic scenarios.
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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 | 0.030 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".