Quantifying age-reading error for use in fisheries stock assessments, with application to species in Australia’s southern and eastern scalefish and shark fishery
Bibliographic record
Abstract
Age-reading error occurs when estimates of age based on reading hard structures differ from the true age of the animal concerned. This error needs to be accounted for when conducting stock assessments. Common methods for quantifying age-reading error include the average percent error, the coefficient of variation, age bias plots, and age difference tables, but these techniques cannot be used to construct age-reading error matrices. A method for constructing age-reading error matrices that accounts for both ageing bias and ageing imprecision is outlined. Simulation evaluation of this method suggests that it is able to estimate both ageing bias (assuming that one reader is unbiased) and ageing imprecision for relatively large sample sizes and for the ages that constitute the bulk of the ages in the sample. However, the performance of the method is poor when sample sizes are small, age-reading error is correlated among readers, when both readers are biased, and for ages that are poorly represented in the sample. The method is applied for illustrative purposes to data on multiple-aged fish in Australia’s southern and eastern scalefish and shark fishery.
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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 | 0.023 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".