Estimation of the Von Bertalanffy Growth Model When Ages are Measured With Error
Bibliographic record
Abstract
Summary The Von Bertalanffy (VB) growth function specifies the length of a fish as a function of its age. However, in practice, age is measured with error which introduces problems when estimating the VB model parameters. We study the structural errors-in-variables (SEV) approach to account for measurement error in age. In practice the gamma distribution is often used for unobserved true ages in the SEV approach. We investigate whether SEV VB parameter estimators are robust to the gamma approximation of the distribution of true ages. By robust we mean a lack of bias due to measurement error and model misspecification. Our results demonstrate that this method is not robust. We propose a flexible parametric normal mixture distribution for the true ages to reduce this bias. We investigate the performance of this approach through extensive simulation studies and a published data set. Computer code to implement the model is provided.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".