Unified natural mortality estimation for teleosts and elasmobranchs
Bibliographic record
Abstract
Natural mortality, M, is a key parameter for the assessment and management of living resources but is difficult to observe directly. Therefore, M is often estimated indirectly from life history traits, and it is typically assumed to be invariant over size, age, and time. Such indirect estimators are particularly relevant for data-poor species, including many elasmobranchs (sharks, skates, and rays). However, as commonly used estimators were developed largely with teleost (bony fish) data, their performance for elasmobranchs is currently unknown. Here, we show that the relationship between observed maximum age, tmax, and mean adult M is not significantly different between teleosts (n = 105) and elasmobranchs (n = 15). Furthermore, data on 16 teleosts and 2 elasmobranchs suggest that juvenile M can be estimated from adult M when juvenile M is inversely proportional to body length and when a reference length can be provided. We introduce this reference length as the length at the age after which M is assumed to be constant and demonstrate how it can be estimated using the von Bertalanffy growth function and the proportion surviving to tmax, which is shown to be approximately 1-2%. The data utilized here also suggest that if tmax is unknown it can be estimated from growth information by assuming that 99% of the asymptotic maximum length is reached at tmax. Based on these life history parameters, the same indirect M estimators can be utilized for teleosts and elasmobranchs, which may contribute to more reliable assessments of data-poor species.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".