Using length–mass relationships to estimate life history: an application to deep-sea fishes
Bibliographic record
Abstract
Length–mass relationships, while often overlooked, form the basis of many fisheries science applications. Fisheries-independent research surveys compile large databases of biological data that could hold a wealth of information regarding species’ life history, which, for many, are data deficient and infrequently sampled. A flexible model using permutations of a broken stick and variance shift was applied to length–mass relationships to evaluate changes in the mean or variability of mass-at-length using data from deep-sea fishes and focusing on particularly poorly known deep-sea chondrichthyans. Changes in body shape and (or) in the scale of variability around mean mass-at-length were estimated for most species (94% of data sets examined). Such changes seemed likely to be correlated with biological factors, such as the onset of reproduction; 70% of length estimates for a variance shift correlated with the expected length-at-maturity. The model presented here could be applied to any fish where length and mass data are available, providing a way to estimate, validate, and investigate biological factors in species where macroscopic evaluations are unavailable or difficult to estimate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".