Metrics and sampling designs for detecting trends in the distribution of spawning Pacific salmon (<i>Oncorhynchus</i> spp.)
Bibliographic record
Abstract
The distribution of individuals among populations and in space may contribute to their resilience under environmental variability. Changes in distribution may indicate the loss of genetically distinct subpopulations, the deterioration of habitat capacity, or both. The distribution of Pacific salmon ( Oncorhynchus spp.) among spawning locations has recently been recognized as an important component of status assessment by USA and Canadian management agencies, but metrics of spawning distribution have not been rigorously evaluated. We evaluated three metrics of spawning distribution and four sampling designs for their ability to detect simulated contractions in the production of coho salmon ( Oncorhynchus kisutch ). We simulated population dynamics at 100 sites using a spawner–recruit model that incorporated natural variability in recruitment, age-at-maturity, dispersal, and measurement error in observations of abundance. Sensitivity analyses revealed that high observation error and straying of spawners from their natal streams may mask changes in distribution. Furthermore, monitoring only sites with high spawner abundance, as is often practiced, failed to capture the simulated contraction of production, emphasizing the importance of matching monitoring programs with assessment objectives.
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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 | 0.052 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".