A generalized application of the catch-curve regression with comparisons of adult mortality and year-class strength between hatchery-stocked and wild-reared lake trout in US waters of Lake Huron
Bibliographic record
Abstract
The recently developed approach to estimating the instantaneous total mortality of coded-wire-tagged lake trout ( Salvelinus namaycush) is generally applicable to catch-at-age data. We further formalized the technique to objectively incorporate the year-class and year effects into the model structure of catch-curve regression. We used this new method to compare adult mortality and year-class strength between the hatchery-stocked and wild-reared lake trout in US waters of Lake Huron, one of the Laurentian Great Lakes. Model comparisons showed no difference in adult mortality between the hatchery-stocked and wild-reared lake trout. Based on 95% confidence intervals, the estimate of adult mortality using the simple catch-curve regression with average number-at-age was not statistically different from the estimate using the linear mixed model with individual number-at-age of multiple year-classes and sampling years. The linear mixed model, however, also quantified lake trout year-class strength and indicated that since 2003, the increases in recruitment of wild-reared lake trout did not fully compensate for the rapid declines in recruitment of hatchery-stocked lake trout 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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".