Challenges in fish aging: the role of otolith preparation technique and experience level in aging lake whitefish (<i>Coregonus clupeaformis</i>)
Bibliographic record
Abstract
Lake whitefish ( Coregonus clupeaformis (Mitchill, 1818)) is an important commercial and recreational species in the Great Lakes. Precise age estimates are important for management, and two widely used techniques for otolith preparation are thin-section and crack-and-burn, which have not been compared for lake whitefish. Sagittal otoliths were collected from 92 lake whitefish in Green Bay and Lake Michigan and aged using thin-section and crack-and-burn techniques. Otoliths were aged independently by three individuals (two novices and one expert) to assess repeatability in estimated ages. Our investigation highlights the inherent difficulty of aging lake whitefish, where thin-section produced significantly older estimated ages (6–30 years) compared to crack-and-burn (5–26 years). Percent agreement of estimated ages between preparation techniques was low for all readers within ±0 years but increased when tolerance buffers were applied. Mean coefficient of variation values from both experience levels (>10.8%) exceeded the acceptable range reported in the literature (5%–7%); however, species longevity and nature of the structure must be considered when establishing target values. Variation in estimated ages is attributed to the experience level and interpretation of structural features. Species-specific training and establishing an objective framework to identify annuli will improve precision metrics.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".