Liming restores Atlantic salmon (Salmo salar) populations in acidified Norwegian rivers
Bibliographic record
Abstract
Acidification has exterminated or seriously reduced Atlantic salmon ( Salmo salar ) populations in 40–45 Norwegian rivers. As this problem still exists, liming to restore salmon has been necessary, which now involves 21 of these rivers. Thirteen rivers were electrofished 1 year before liming and again 1–12 years later. There was a significant effect both of time after liming and status (e.g., formerly lost and reduced stocks) on the densities of both fry (age 0+) and parr (age ≥ 1+). However, the rate of increase in densities of young salmon in these two status categories was not significantly different in either age group. The development in parr densities suggests that more than 20 years of liming is required to restore salmon in rivers with lost native populations. Stocked rivers and rivers unaltered by hydropower developments generally had higher fry densities and faster increase in parr densities. Annual rod catches of adult salmon increased significantly after liming started, reaching about 45 t after 10 years of treatment. This is 11%–12% of the current total catch of Atlantic salmon in all Norwegian rivers. Liming thus makes an important contribution to the restoration of salmon in formerly acidified rivers.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".