Terrestrial liming to promote Atlantic Salmon recovery in Nova Scotia – approaches needed and knowledge gained after a trial application
Bibliographic record
Abstract
Abstract. Populations of Atlantic salmon (Salmo salar) in Southwest Nova Scotia (SWNS) have plummeted since the 1980s. Acidification is considered a main threat to this population. The lakes and streams of SWNS were among the most heavily acidified in North America during the last century and calcium levels are predicted to continue to fall in coming decades. One of the most promising mitigation options to reduce the risk of extirpation of the SWNS Salmo salar is terrestrial liming; however, both the chemistry of SWNS rivers, and effective strategies for terrestrial liming in SWNS are poorly understood. Here we have launched the first terrestrial liming study in Nova Scotia, employing a test hydrologic source area liming strategy in a 5 ha experimental catchment in SWNS, Maria Brook; we apply an average local application rate of 13 t ha−1 to 10% of the 47 ha catchment. We employ high frequency stream monitoring to complement grab sampling to identify which constituents pose a threat to Salmo salar and to identify strategies for larger scale terrestrial liming that would fit the local conditions. Results indicate that the water chemistry conditions are currently at toxic levels for Salmo salar throughout the year, with levels of ionic aluminium exceeding toxic thresholds almost 100% of the time. The stream chemistry in Maria Brook is remarkably similar to pre-recovery conditions in other heavily acidified watersheds, such as Birkenes in Norway. Our results support the hypothesis that there has been no recovery from acidification in SWNS. Results from the first year of post-liming do not show an improvement in stream chemistry levels, and further lime application is needed to improve the water chemistry conditions to needed levels for the recovery of Salmo salar.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".