Predicting the impact of perturbations on salmon (<i>Oncorhynchus</i> spp.) communities: implications for monitoring
Bibliographic record
Abstract
Twenty-six salmon (Oncorhynchus spp.) stocks from the Pacific Northwest are listed as either threatened or endangered. A number of anthropogenic factors, likely including degradation of habitat by chemical contaminant exposure, have contributed to their decline. Techniques that can assess injury or judge the efficacy of regulatory actions on the recovery of this species are needed. We strive to understand why a population is changing by examining changes in their intrinsic birth rates, death rates, and (or) growth rates. However, salmon populations are influenced by other species in the community. To address this issue, we developed a parsimonious three-trophic-level community model consisting of prey, salmon, and parasites and examined the model's response to one anthropogenic factor (contaminant exposure) using qualitative analysis. This community model may not only provide valuable insight into salmon survival but also may broaden the approaches available to elucidate direct and indirect effects. We demonstrate analytically that some community members, possibly salmon themselves, might be ambiguous or unreliable variables to monitor. We also demonstrate that other species in the community, such as parasites, may be more sensitive than salmon in monitoring the influence of anthropogenic factors such as contaminants.
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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 | 0.005 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".