Age estimation comparison between whole and thin-sectioned otoliths and pelvic fin-ray sections of long-lived lake trout, Salvelinus namaycush, from Great Bear Lake, Northwest Territories, Canada
Bibliographic record
Abstract
Abstract Studies to determine precision and bias of both methods and age-readers are important to evaluate reliability of age data used for developing fisheries management objectives. We assessed within-reader, between-reader, and between-method precision (coefficient of variation, CV%) and bias of age estimations for long-lived lake trout, Salvelinus namaycush, from Great Bear Lake using three readers with different levels of experience. The assessment used independent age estimates (n = 3 per reader) from whole and transverse-sectioned otoliths (range = 1–67 years), and pelvic fin-ray sections (range = 3–26 years). We also examined between-method differences in assigned confidence scores. Within readers, age estimates from sectioned otoliths were more precise (2.6–3.0%) than whole (3.6–4.5%) otoliths. Between whole and sectioned otoliths, precision of age estimates was 5.4% and bias was low up to age 20. Age was typically under-estimated from whole otoliths compared to sections for fish ≥ 34 years. Increased reader confidence was correlated with greater precision and younger age estimates, particularly for whole otoliths, but less so for fin rays. Age was estimated with higher confidence from otolith sections than other methods. The least experienced reader estimated age with the lowest precision, and between-reader bias was evident among older ages. Age was consistently under-estimated and less precise from pelvic fins compared to sectioned otoliths, and are therefore an unsuitable non-lethal alternative. Sectioned otoliths revealed longevity was greater (67 years) than historically documented using whole otoliths (53 years) for these fish. Our findings contribute to those relying on otoliths or pelvic fin rays to estimate ages of long-lived lake trout populations, which are a key component of freshwater fauna in polar North America.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".