Estimation of growth parameters based on length-stratified age samples
Bibliographic record
Abstract
Response-selective stratified sampling (RSSS) has been well studied in the statistical literature; however, the application of the resulting statistical theories and methods to a specific case of RSSS in fisheries studies, namely length-stratified age sampling (LSAS), is inadequate. We review nine estimation approaches for RSSS found in the statistical and fisheries science literature in terms of three sampling components: the first phase length composition sample, the second phase age composition sample, and the sampling scheme. We compare the performance in terms of RRMSE (relative root mean squared error) for von Bertalanffy (vonB) growth model parameter estimation using an extensive simulation study. We further demonstrate methods by applying the two best-performing and the most popular methods to estimate the vonB model parameters for American plaice (Hippoglossoides platessoides) in NAFO Divisions 3LNO. Our simulations demonstrated that mis-specifying one or more of the three sampling components increases the RRMSEs, and this effect is magnified when the age distribution is incorrectly specified. The optimal approach for data based on LSAS is the empirical proportion approach, and we recommend this method for growth parameter estimation based on LSAS data.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".