Bibliographic record
Abstract
Abstract The 29 estimators of natural mortality (M) that have been proposed for ‘information‐limited’ fisheries are reviewed, together with a new alternative presented here. Each is applied to 13 example populations for which well‐founded estimates are available of bothMand the estimators' parameters. None of the 30 can provide accurate estimates for every species, and none appears sufficiently precise for use in analytical stock assessments, while several perform so poorly as to have no practical utility. If the growth coefficientKhas been reliably estimated, eitherM = 1.5 Kor Pauly's long‐established estimator can provide useful estimates ofM,but they fail with species that have long adult lives after swift juvenile growth, with those that never reach their asymptotic lengths and with species that otherwise deviate from archetypal teleost life histories. If a pre‐exploitation maximum observed age (Tmax) can be established,Mcan be estimated for both teleosts and sharks usingM = 4.3/Tmaxbut that seriously underestimates when the effective sample size (ne) is large and overestimates with species showing pronounced senescence. The new estimator presented here addressesnebut is upset by even mild senescence. Some estimators ofM‐at‐size, particularly ones recently advanced by Gislasonet al. and Charnovet al., also show promise but require further examination. It is recommended that fisheries scientists measureMby more advanced methods whenever possible. If ‘information‐limited’ estimators must be used, their uncertainties should be acknowledged and their errors propagated into management advice.
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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 | 0.012 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".