A stage-explicit expression of the von Bertalanffy growth model for understanding age at first reproduction of Great Lakes fishes
Bibliographic record
Abstract
An initial annual growth rate of body length and its regular decrease with increasing age has general linkages with age at first reproduction (tR). We clarify their combinations and develop predictive functions. We use a complete FordWalford plot with yearling size (L1) on the y axis and show a slope transition between the relative yearling growth rate (ρy) and the FordWalford slope (ρ). The three stage-specific variables define a complete body-length trajectory over ages, including all von Bertalanffy growth parameters and the FordWalford intercept (Lint). The difference between asymptotic length (Linf) and yearling length is growth potential after the first annulus. Yearling growth is a transition period, so growth potential can be adjusted as ρLinf or Linf Lint. Changes in the three life-stage variables have contrasting effects on growth potential and von Bertalanffy growth parameters, so they have contrasting relations with tR. For most invertebrate-eating fishes in the Laurentian Great Lakes, dominant changes in growth trajectories were reflected in ρ, so tR was predicted by the von Bertalanffy growth coefficient, K. For walleye (Stizostedion vitreum) populations around the Great Lakes, dominant changes in growth trajectories were from yearling size or yearling growth, so tR was predicted using Lint. Our results have clear implications for understanding fish population dynamics.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".