Time varying M with starvation mortality in a state-space stock assessment model: Part 2: Atlantic cod (Gadus morhua) on the southern Grand Bank of Newfoundland
Bibliographic record
Abstract
State-space models are now a common tool for modeling time-varying ecological phenomena. This extends to state-space stock assessment models (SSAMs), recognized as pivotal components within the evolving landscape of next-generation stock assessment methodologies. Though methods are rapidly evolving, the estimation of time-varying rates of natural mortality ( M ) remains a challenge, and the sensitivity of stock assessments and management advice to assumed M values underscores the pressing need for improved estimation methods. Using southern Grand Bank (SGB) Atlantic cod as a case study, we introduce a novel approach to estimate time-varying M . We first convert a length-based starvation M index into an age-based index, which we then include in an age-based SSAM to estimate two components of M: starvation M and a remainder component. This produces a new SGB cod SSAM with time-varying total stock M . This model produces a large decrease (68 %) in the size of the model process errors (i.e., their standard deviation) and better fit compared to a model that did not account for time-varying M , indicating that the starvation M index improves our model of stock productivity. By leveraging readily available information on fish body condition and the proportion of fish in really poor condition, the proposed methods offer a valuable solution to the challenges associated with estimating time-varying M. The proposed methods offer a tractable solution to the common struggles associated with quantifying changes in fish productivity, which is crucial for the management of dynamic systems.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".