Comment on “A new approach for estimating stock status from length frequency data” by Froese et al. (2018)
Bibliographic record
Abstract
Abstract Potential users of the model proposed by Froese et al. (2018) should be aware of several issues. First, the method to calculate equilibrium numbers-at-length is incomplete and leads to negatively biased estimates of fishing mortality. Second, inadequate simulation testing fails to reveal that the method is highly sensitive to assumptions of equilibrium conditions and that the population average asymptotic length (L∞) can be approximated by the largest observed size (Lmax). Finally, the Froese et al. (2018) model relies on the assumption that the ratio of natural mortality (M) to the von Bertalanffy growth parameter (K; M/K) is typically around 1.5, which they argue is supported by the literature for most fish stocks. We believe that this conclusion is based on an insufficient reading of the literature and, in fact, there is strong evidence to support the claim that M/K is outside the narrow bounds of 1.2–1.8 for many exploited species. Potential users of the method are alerted to these issues and alternative approaches are recommended to avoid these biases.
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 | 0.021 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.039 | 0.055 |
| Insufficient payload (model declined to judge) | 0.010 | 0.011 |
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".