The von Bertalanffy growth function, bioenergetics, and the consumption rates of fish
Bibliographic record
Abstract
The von Bertalanffy growth function (VBGF) is based on a bioenergetic expression of fish growth; therefore, size-at-age data can theoretically be used to estimate fish consumption rates. We evaluated the accuracy of VBGF-derived consumption rates by performing a meta-analysis and sensitivity analysis of VBGF assumptions, and we used Bayesian parameter estimation to quantify uncertainty in these estimates. The VBGF was robust to its assumption regarding the allometry of catabolism but was highly sensitive to the assumed allometry of consumption. Consequently, the commonly used form ("specialized" VBGF), which makes a strong assumption regarding the allometric slope of consumption, often grossly underestimates (>50%) consumption. The precision of the VBGF depended on characteristics of the size-at-age data used to parameterize the model. When data indicate decelerating growth, consumption rates were estimated with good precision; we estimated a 70% probability that bluefin tuna (Thunnus thynnus) consumption rates were between 1 and 2% body mass per day. Otherwise, consumption estimates were poorly defined; yellowfin tuna (Thunnus albacares) consumption rates between 2 and 7% per day were all equally likely. We conclude that VBGF can be a useful tool for estimating fish consumption rates, but potential biases and precision of these estimates should be evaluated on a case-by-case basis.
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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 | 0.053 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.012 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".