The longer the better? Trade‐offs in fisheries stock assessment in dynamic ecosystems
Bibliographic record
Abstract
Abstract Environmental change and anthropogenic activity, alone or in combination, may cause vital rates of fish populations to exhibit low‐frequency, large‐magnitude variation leading to non‐stationary population processes in inherently dynamic ecosystems. It remains unclear why, when and, so, whether, non‐stationary population processes should be taken into account in fisheries stock assessment models. Here, we clarify the necessity and conditions for including non‐stationary population processes in stock assessment models. Specifically, the convention of treating population vital rates with constant parameters might be unreliable under regime shifts characterized by low‐frequency, large‐magnitude variation in drivers of fish population dynamics. We hypothesized and simulated effects of a U‐shaped trade‐off between the length of time‐series data and model estimation error for fish populations exhibiting non‐stationary dynamics. In the case of low‐frequency, large‐magnitude variation, when measurement errors were small, the effects of non‐stationary population processes were stronger than otherwise. Including time‐varying parameters of population vital rates in the assessment model resolved this dilemma as anticipated, albeit at the cost of increased model complexity, suggesting that accounting for non‐stationarity with time‐varying parameters alone may not be sufficient to improve stock assessments and other approaches should also be considered.
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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 | 0.023 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".