Can nutrient additions facilitate recovery of Pacific salmon?
Bibliographic record
Abstract
Multiple restoration actions have been implemented in response to declining salmon populations. Among these is the addition of salmon carcasses or artificial nutrients to mimic marine-derived nutrients historically provided by large spawning runs of salmon. A key assumption in this approach is that increased nutrients will catalyze salmon population growth. Although effects on aquatic ecosystems have been observed during treatments, it is unclear whether permanent population increases for salmon will occur. To test this assumption and address associated uncertainties, we linked a food web model with a salmon life cycle model to examine whether carcass additions in a river reach would improve conditions for salmon in the long term. Model results confirmed immediate increases in the biomass of periphyton, macroinvertebrates, and fish during carcass additions. In turn, juvenile salmon grew larger and experienced improved freshwater and smolt survival, which translated to a greater number of adults returning to spawn. However, once additions ceased, salmon abundance returned to pretreatment levels, which, based on our model, is owing to a combination of instream and out-of-basin factors. Overall, results of this work suggest that benefits during carcass and nutrient additions may not translate into persistent productivity of salmon unless additions are sustained indefinitely or other limiting factors are addressed.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".