A Comparison of the Scale and Otolith Methods of Age Estimation for Lake Whitefish in Lake Huron
Bibliographic record
Abstract
Abstract We compared sagittal otolith and scale age estimation methodologies for lake whitefish Coregonus clupeaformis collected in Georgian Bay and the main basin of Lake Huron between 2002 and 2004 in terms of the age, growth, and mortality estimates generated by the two methodologies. In general, otolith age estimates were higher than scale age estimates. Forty-nine percent of the fish aged by otoliths were judged to be greater than 10 years of age, compared with 5% of the fish aged by scales, and more age-classes were found when otoliths were used. Otolith and scale ages agreed for 16% (n = 60) of Georgian Bay fish and only 9% (n = 27) of main-basin fish. The overall coefficients of variation for the otolith and scale age estimation methodologies pooled across years and basins were 5.52% and 2.68%, respectively. Mean length at age based on otoliths was significantly lower than mean length at age based on scales. Variation in the mean length at age was greatest for fish age 7 and older. Otolith-based catch-curve estimates of total instantaneous mortality (Z) were 1.26 in Georgian Bay and 0.57 in the main basin. In contrast, scale-based estimates of Z were 0.98 for Georgian Bay and 0.85 for main-basin lake whitefish. This study has demonstrated that estimates of age, growth, and mortality for lake whitefish in Lake Huron vary according to aging methodology. Therefore, we recommend that mark–recapture studies be undertaken to validate the spatiotemporal variation in lake whitefish age estimates in Lake Huron.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".