Modelling length-at-age variability under irreversible growth
Bibliographic record
Abstract
In this paper, we describe a discrete-time formalism for describing the dynamics of the size-at-age distribution of a cohort of individuals exhibiting irreversible von Bertalanffy growth in a statistically uniform random environment. This formalism yields a highly efficient numerical implementation, which is particularly suited to automatic optimization. In the special case where mortality is sufficiently size-independent not to vary substantially across the bulk of the size distribution at any given age, we can further increase this efficiency by deriving compact update rules for the mean and coefficient of variation of size-at-age. In this case, we also demonstrate that the depensatory effect of random growth variability and the compensatory effect of deterministic von Bertalanffy growth balance to yield an attracting (initial condition independent) trajectory of mean length and length coefficient of variation against age. We demonstrate the applicability and extensibility of this formalism by two exemplary applications - juvenile salmonids and demersal cod.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".