Estimation and analysis of biological parameters in elasmobranch fishes: a comparative life history study
Bibliographic record
Abstract
Published life-history parameters for sharks, skates, and rays over a wide geographic range were used to develop predictive models to estimate parameters that are difficult to measure or have not been previously estimated in elasmobranch species. We determined empirical relationships between body size (total length) and length at maturity (L m ) and age at maturity (T m ). The data used in determining these empirical relationships, the von Bertalanffy parameters asymptotic length (L [Formula: see text] ) and growth rate (k), and natural mortality (M) and maximum age (T max ) were used to describe the life-history strategies of elasmobranch fishes. M/k and Beverton's growthmaturitylongevity plots were used to make comparisons between teleost fishes, reptiles, and elasmobranchs. We found that the M/k ratio in elasmobranchs is significantly different from those for other fish and reptile taxa. We linked elasmobranch species fecundity (f) and T m to potential vulnerability to population decline under exploitation. We found that larger species of elasmobranchs have lower growth rates (k) and potential population increases (r'). Elasmobranchs can be categorized by species maximum length to determine susceptibility of decline under exploitation. Generally, species greater than 100 cm are characterized by life-history and population traits that place them at greater risk of population decline.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".