Improving assessment of <i>Pandalus</i> stocks using a seasonal, size-structured assessment model with environmental variables. Part I: Model description and application
Bibliographic record
Abstract
Pandalus species display the following features that make it difficult to apply traditional age-based stock assessment models: (i) difficulty of determining age in the absence of hard parts retained through the molt; (ii) sex change in which individuals mature first as males and then transform to females; and (iii) potentially strong influence of environmental conditions on recruitment population dynamics. In this context, we propose a seasonal, size-structured assessment model dedicated to stock assessment of hermaphroditic Pandalidae. The modeling framework incorporates a submodel for changes of length at sex transformation and functions to incorporate environmental effects on recruitment dynamics. The model can be directly fitted to length-structured data, overcoming the length to age conversion problem. The model has a seasonal time step that allows it to account for seasonal variations in biological processes and fishing patterns. The model provides stock assessment outputs, such as fishing mortality and stock biomass estimates, and sex-specific abundance-at-length. The model is applied to the exploited shrimp stock of Pandalus borealis in the Gulf of Maine as an example of its utility. The model proposed in this study is flexible and generic and can be applied to many other exploited stocks.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".