Can spawning origin information of catch or a recruitment penalty improve assessment and fishery management performance for a spatially structured stock assessment model?
Bibliographic record
Abstract
We used simulations based on lake whitefish (Coregonus clupeaformis) populations to explore the benefits of using spawning origin information for parsing catch to spawning populations in stock assessments for intermixed fisheries exhibiting an overlapping movement strategy. We compared this origin-informed assessment model with a standard assessment model that did not parse catch. We additionally evaluated the influence of including annual recruitment penalties. For standard assessment models, spawning stock biomass estimates could be unstable and biased (sometimes by more than 50%), depending upon population mixing and productivity, and in some cases estimated near average zero recruitment in the terminal year. Incorporating information on population-specific harvest age composition improved spawning stock biomass estimation (e.g., by sometimes essentially removing 50% biases and improving accuracy). Assessments with recruitment penalties produced less biased terminal recruitment estimates (sometimes a 100% bias was removed). Under status quo target mortality rates, improvements in assessments did not necessarily translate to improved fishery management performance (e.g., avoiding depletion of spawning biomass), but such improvements, and overall better performance, were seen at lower target mortality rates.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".