Guidance on when and how science advice for Pacific salmon should account for time-varying population dynamics
Bibliographic record
Abstract
Time-varying population dynamics are ubiquitous for Pacific salmon due to, for example, changing ocean conditions and degradation of freshwater habitats. When these changes are ignored, science advice may result in poor biological outcomes or failure to meet catch objectives. There is currently very little guidance on where, when, and how to account for them in science advice for Pacific salmon. We reviewed the literature and performed computer simulations to determine when and how stationary and time-varying assessment models should be applied to inform science advice for Pacific salmon. When seeking to estimate population parameters from spawner-recruitment relationships, we found that models that annually track changes in productivity have better statistical reliability than stationary models or those that assume abrupt regime shifts. However, when models with time-varying parameters are applied for assessment or management purposes (e.g., as management reference points) under irreversible declines in productivity, they tend to be associated with increased biological risk relative to those that assume stationary dynamics. We recommend using time-varying models for assessment purposes only when the weight-of-evidence from multiple sources supports time-varying dynamics. Further, management actions that account for time-varying parameters should been evaluated in a risk-based, decision-making framework against those that assume stationary dynamics.
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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 | 0.057 | 0.189 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.014 | 0.010 |
| Insufficient payload (model declined to judge) | 0.034 | 0.018 |
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".