An age‐ and length‐structured statistical catch‐at‐length model for hard‐to‐age fisheries stocks
Bibliographic record
Abstract
Abstract It is challenging in fisheries stock assessment to estimate cohort dynamics from length‐based data for hard‐to‐age stocks, and existing approaches, for example, age‐structured catch‐at‐length models (ACL) are unable to account for length‐dependent processes within each cohort. Fisheries‐dependent data are usually considered the default input to stock assessment models. However, with widespread recognition of the uncertainty of fisheries‐dependent data and the increasing availability of high‐quality survey data, a new situation emerges in some fisheries where a stock assessment model based only on survey data can provide good estimation of population dynamics. We develop an age‐ and length‐structured statistical catch‐at‐length model (ALSCL) to estimate age‐based dynamics from survey catch‐at‐length data. This approach also provides a good basis to then integrate fisheries‐dependent data in the model. ALSCL can explicitly include length‐dependent mortality and growth within each cohort by simultaneously tracking the three‐dimensional dynamics across time, age, and length. We first use simulations of yellowtail flounder (Limanda ferruginea, Pleuronectidae) and bigeye tuna (Thunnus Obesus, Scombridae) to demonstrate that ALSCL outperforms ACL by providing more accurate estimates of age‐based population dynamics when length‐dependent processes are important. Next, we apply ALSCL to estimate the cohort dynamics of female yellowtail flounder on the Grand Bank off Newfoundland using survey catch‐at‐length, weight‐at‐length, and maturity‐at‐length data. We consider ALSCL as a hybrid between ACL and length‐structured stock assessment models that keeps the advantages of both, and its ability to simultaneously track age and length dynamics is an important step toward the next‐generation of fisheries stock assessment models.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".