Bibliographic record
Abstract
Summary Somatic growth is a fundamental property of living organisms, and is of particular importance for species with indeterminate growth that can change in size continuously throughout their life. For example, fishes can increase in size by 2–6 orders of magnitude during their lifetime, resulting in changes in production, consumption and function at the ecosystem scale. Within species, growth rates are traded off against other life‐history parameters, hence an accurate description of growth is essential to understand the comparative demography, productivity, fisheries yield and extinction risk of populations and species. The growth trajectory of indeterminate growing sharks and rays (elasmobranchs) and bony fishes (teleosts) is usually modelled using a three‐parameter logarithmic function, the von Bertalanffy growth function (VBGF), to describe the total length of the average individual at any given age. Recently, however, a two‐parameter form has gained popularity. Rather than being estimated in the model fitting process, the third y‐intercept parameter (L0) of theVBGFhas been interpreted as being biologically equivalent to, and thus fixed as, the empirically estimated size at birth. We tested the equivalence assumption thatL0is the same or similar to size at birth by comparing empirical estimates of size at birth available from the literature with estimates ofL0from published data from elasmobranchs, and found that even though there is an overlap of values, there is a high degree of variability between them. We calculate the bias in the growth coefficient (k) of theVBGFby comparison between the two‐ and three‐parameter estimation methods. We show that slight deviations in fixedL0can cause considerable bias in growth estimates in the two‐parameterVBGFwhile providing no benefit even whenL0matches the true value. We show that the effect of this biased growth estimate has profound consequences for fisheries stock status. We strongly recommend the use of the three‐parameterVBGFand discourage use of the two‐parameterVBGFbecause it results in substantially biased growth estimates even with slight variations in the value of fixedL0.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".