Performance of age-only state-space assessment models under diverse somatic growth scenarios
Bibliographic record
Abstract
Recent developments have allowed state-space assessment models (SSAMs) to incorporate processes such as growth, size-based selectivity and maturity; however, many assessments continue to approximate them as age-based (“age-only” SSAMs). In this study, we use a simulation experiment to evaluate how different factors related to the sampling scheme and the type of growth variability affect the performance of age-only SSAMs. We followed two simulation approaches: “traditional”, which assumes all processes in the simulation are age-based, and “stepwise”, which aims to approximate the age–length dynamics and sampling process. We found that the traditional approach may produce overly optimistic performance by ignoring the age–length dynamics. Also, a length-stratified sampling scheme for ageing improves recruitment estimates, while a random sampling scheme may be preferable for estimating population mean weight-at-age. Modelling time-varying selectivity when variability in somatic growth is present is critical to improving recruitment variability and SSB estimates. Our results offer practical guidance when implementing SSAMs with age-specific data and highlight the importance of accounting for growth dynamics and sampling design in the assessment 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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".