Form and uncertainty in stock–recruitment relations: observations and implications for Atlantic salmon (Salmo salar) management
Bibliographic record
Abstract
This paper reports an investigation of stock–recruitment relations for Atlantic salmon ( Salmo salar ). We regard these relations as stochastic functions characterized by an expected stock–recruitment relation and deviations from this expectation driven by observational error and uncharacterised environmental variability. We estimate model parameters by standard Bayesian methods. Analysis of the input–output characteristics of segments of the salmon life cycle in the Girnock Burn (Northeast Scotland) reveals two independent regulatory processes, one between ova and fry and the other between fry and smolts. Comparison of stock–recruitment relations for Atlantic salmon in Scotland, Ireland, and Canada, reinforced by an extended series of simulation studies, shows that even when comparatively long time series of high quality data are available, it is frequently difficult to exclude the possibility of low stock depensation — an effect whose implication of enhanced extinction risk implies that precautionary management policy would pay special attention to the posssibility of its occurence. A particular feature of our simulation results is their demonstration that inappropriate combination of distinct subpopulations both increases process noise and distorts the expected stock–recruitment relation, thereby greatly reducing the accuracy of any system identification process.
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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 | 0.007 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".