Predicting changes in mean length with an age‐structured stock assessment model
Bibliographic record
Abstract
Abstract Several studies have shown that mean length is only slightly biased and hence a robust indicator of the total mortality (fishing and natural mortalities). However, these studies use models that typically assume equilibrium conditions and are expected to predict a much stronger relationship between mean length and fishing mortality than would be obtained from a model with more realistic assumptions. Here we predict changes in annual mean length with stochastic stock reduction analysis (SRA)—an age‐structured model—that accounts for recruitment variation around an average stock‐recruitment relationship and time‐varying exploitation rate. We found that SRA‐predicted mean length fluctuates considerably over exploitation rate, as opposed to equilibrium mean length, which consistently declined as exploitation rate climbed. Our finding suggests that the inference obtained from examining the relationship between fishing effects and mean length under equilibrium conditions could be misleading. Recommendations for resource managers Previous studies showed that equilibrium mean length—that is, assuming continuous and constant recruitment and constant total mortality over time—is a reliable indicator of fishing and natural mortalities, where changes in the observed mean length mimic the total mortality of the harvested population. By estimating mean length under sensible assumptions, such as accounting for recruitment variability around an average stock‐recruitment relationship, and time‐varying exploitation rate (fishing mortality), high exploitation rate does not necessarily result in a low mean length. Given that equilibrium approaches to estimate mortality as a function of mean length are still appealing, due to their simplicity and basic data requirement, caution must be exercised when interpreting the results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".