Predicting climate change effects on subarctic–Arctic populations of Atlantic salmon (<i>Salmo salar</i>)
Bibliographic record
Abstract
We predict an increase in parr recruitment and smolt production of Atlantic salmon (Salmo salar) populations along a climate gradient from the subarctic to the Arctic in western and northern Norway in response to future climate change. Firstly, we predicted local stream temperature and discharge from downscaled data obtained from Global Climate Models. Then, we developed a spatially explicit individual-based model (IBM) parameterized for the freshwater stage, with combinations of three different postsmolt survival probabilities reflecting different marine survival regimes. The IBM was run for three locations: southern Norway (∼59°N), western Norway (∼62°N), and northern Norway (∼70°N). Increased temperatures under the future climate regimes resulted in faster parr growth, earlier smolting, and elevated smolt production in the western and northern locations, in turn leading to increased egg deposition and elevated recruitment into parr. In the southern location, density-dependent mortality of parr resulting from low summer wetted-areas reduced predicted future smolt production in comparison to the other locations. It can be inferred, therefore, that climate change may have both positive and negative effects on anadromous fish abundance within the subarctic–Arctic according to geographical region.
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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 | 0.001 |
| 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 | 0.000 | 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".