Application of dolomite to forested catchments in Nova Scotia improves water quality - but more is needed to meet water quality targets
Bibliographic record
Abstract
Populations of Atlantic salmon (Salmo salar) in Nova Scotia have plummeted in recent decades. One of the major threats for these populations is freshwater acidification, which has caused toxic water conditions including elevated stream water concentrations of toxic cationic aluminum (Ali). The only viable management option to reduce the threats of acidification to Atlantic salmon within the timeline needed to save the remaining populations is the addition of alkaline materials to waters or soils, via “liming.” While studies in Europe, the UK, and the northeastern USA show that stream water Ali concentrations decrease in response to terrestrial liming with positive impacts on fish communities, stream chemistry response to terrestrial liming in Nova Scotia has not yet been examined. Here we examine the response of stream water chemistry to terrestrial liming in two types of experimental treatments in Nova Scotia. Our results show that liming decreased streamwater Ali concentrations and increased dissolved calcium concentrations and pH levels. Untreated sites have water chemistry conditions that are toxic to Atlantic salmon, and although water chemistry was improved at treated sites, some parameters still do not meet thresholds for aquatic health, indicating that higher doses or repeated liming treatments are required. Results suggest that expansion of liming activities with higher liming doses may help avoid loss of the remaining wild salmon populations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".