Admitting ageing error when fitting growth curves: an example using the von Bertalanffy growth function with random effects
Bibliographic record
Abstract
A way to explicitly incorporate ageing error into the estimation of von Bertalanffy growth function (VBGF) parameters using a random effects (RE) modeling framework is presented. This RE framework also accounts for the effects of selectivity on growth curve estimation by characterizing the distribution of true ages derived from multiple age reads using either an exponential or gamma distribution. Simulation testing across four life histories is used to compare the RE approach with standard nonlinear (SNL) approaches that use the primary, average, or median ages in growth estimation. Sensitivity tests compare the effects of assumed length and ageing error, selectivity, and recruitment variability on the estimation of growth curve parameters. Results support the use of the RE method using a gamma distribution over the SNL methods because RE method estimates of VBGF growth parameters were more precise across life histories and sensitivity trials. This general approach can be applied and expanded to other growth models. Applications demonstrate that the results from RE methods may differ in biologically important ways to those obtained from SNL approaches.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".