Landlocked Atlantic salmon in a large river–lake ecosystem: managing an endemic, large-bodied population of high conservation value
Bibliographic record
Abstract
Managing and conserving threatened migratory salmonid populations in large river–lake ecosystems is challenging not only because of the ecosystems’ large size, but also because there is often more than one anthropomorphic stressor. The River Klarälven – Lake Vänern ecosystem, situated in Norway and Sweden, is a large, highly modified ecosystem, home to a threatened, endemic, large-bodied population of landlocked Atlantic salmon (Salmo salar). With 11 dams, the salmon population has been maintained through extensive stocking and a truck and transport system for spawners. Here we review what we have learned about the salmon after 15 years of research, highlighting the major findings for each life stage. Our studies indicate that the salmon population is below carrying capacity, and we suggest measures to increase the number of spawners and downstream passage success. Habitat restoration to compensate for losses from former log-driving activities is expected to further increase carrying capacity. Re-establishing salmon in Klarälven’s upper reaches in Norway, while possible, is fraught with both ecological and legislative hurdles. Substantial long-term funding is needed to foster co-management and ensure a sustainable fishery.
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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 | 0.001 | 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) | 0.001 | 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".