Freshwater salinization of seasonal ponds: High salinity and stratification threaten critical, overlooked habitats
Bibliographic record
Abstract
Nearly a century of road salt use in the snowbelt region of North America has led to substantial increases in salinity levels in freshwater habitats (1, 2). Salt pollution in lakes and rivers is well characterized (3, 4). Lacking are broad insights for seasonal ponds. As critical habitats for many endemic species, these small and often poorly flushed surface waters are especially vulnerable to accumulating high levels of salts and other pollutants. Here, we measured salinity in 165 seasonal ponds, characterizing salt pollution patterns across space, through time, and over depth within ponds. We found that 70% of ponds within 37 m of a road contained salinity levels exceeding Canadian federal guidelines. 54% of ponds within 25 m exceeded less conservative US federal guidelines. Within ponds, the water column was stratified due to the combined density effects of salt and temperature. Bottom waters of polluted ponds were about 57% saltier than near surface waters, though many were much saltier than this. Compared to lakes and rivers, far more seasonal ponds appear to be compromised by deicing salt, and overall, the concentration of salt appears to be substantially higher. Among aquatic habitats, seasonal ponds are experiencing the most severe impacts of freshwater salinization with consequent impacts on sensitive aquatic organisms.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".