The precision of otolith radiometric ageing of fish and the effect of within-sample heterogeneity
Bibliographic record
Abstract
Ageing of fish using radiometric methods applied to otoliths is a widely accepted and valuable technique for validating annulus counts. Initially, whole otoliths were analysed, but it is now more frequent, and requires less stringent assumptions, to analyse otolith cores. Data from published studies were used to calculate typical ageing errors, assuming linear growth in otolith mass. These errors increase with increasing age, are much smaller if an improved method for measuring 226Ra is used, and, for older ages, are greater when cores are used (nevertheless, the use of cores, rather than whole otoliths, is recommended because the stronger assumptions required for the latter are hard to justify or verify). It is common to use more than one otolith per sample (sometimes more than 100) so as to provide sufficient sample mass, and to assume no within-sample heterogeneity in otolith age and mass-growth rate. A simulation experiment was carried out to determine whether any violation of this assumption was likely to have a significant effect on the accuracy of estimated ages. Plausible levels of heterogeneity were found to produce only a negligible decrease in precision and small bias (<10%).
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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 | 0.043 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".