Accounting for age measurement errors in fish growth \nmodel estimation using length-stratified age sampling \ndata
Bibliographic record
Abstract
Fish growth models are crucial for fisheries stock assessments and are commonly estimated \nusing fish length-at-age data. This data is widely collected using length-stratified \nage sampling (LSAS), a cost-effective two-phase response-selective method. The data may \ncontain age measurement errors. We propose a methodology that remarkably reduces the \nbias in the estimation of fish growth for LSAS data with age measurement errors. The proposed \nmethods use empirical proportion likelihood methodology for LSAS and the structural \nerrors in variables methodology for age measurement errors. We provide a measure \nof uncertainty for parameter estimates and standardized residuals for model validation. \nTo model the age distribution, we employ a continuation ratio-logit model consistent \nwith the random nature of the true age distribution. We also apply a discretization approach \nfor age and length distributions, which significantly improves computational efficiency and \nis consistent with the discrete age and length data typically encountered in practice. The \nsimulation study shows that neglecting age measurement errors can lead to significant bias \nin growth estimation, even with small but nonnegligible age measurement errors. However, \nour new approach performs well regardless of the magnitude of age measurement \nerrors and accurately estimates standard errors of parameter estimates. Real data analysis \ndemonstrates the effectiveness of the proposed model validation device. Computer codes \nto implement the methodology are provided.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".