The status of Atlantic salmon (<i>Salmo salar</i>): populations and habitats
Bibliographic record
Abstract
The important decline of Atlantic salmon (Salmo salar) across its range during the past three decades, despite numerous management and conservation programmes, is an alarming index of the vulnerability of this species. The following series of papers was produced to summarize current knowledge on specific interactions between biotic and abiotic variables that may contribute to determine the survival of Atlantic salmon. Evaluation of the challenges encountered in spawning grounds (siltation, oxygenation), nursery habitats (substrate, trophic interactions), overwintering habitats (flow conditions, winter feeding opportunities), and coastal and oceanic environments (water temperature, predators, parasites) suggest that all habitats required by Atlantic salmon and all processes that occur in each habitat represent a critical link that allows this species to persist. Management practices employed during artificial fish selection, incubation, and stocking also affect the success of restoration efforts. Because limiting factors may change in time and because our ability to intervene in specific habitats may be minimal, the only strategy within our reach may be to continue gathering information about processes that determine the fragility of Atlantic salmon and, in the light of our findings, to implement scientifically sound actions where and when possible.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".