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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".