Using strain-specific genetic information to estimate the reproductive potential of lake trout spawning biomass in southern Lake Michigan
Bibliographic record
Abstract
Lake trout reproduction has increased in Lake Michigan since the 2000 s. Previous genetic studies reported that the strains of stocked adults did not contribute equally to wild recruits. Consequently, reproductive potential of spawning biomass estimated in stock assessments will depend upon strain composition, complicating comparisons across time and space. We integrated data from a stock assessment with genetic data to estimate an effective lake trout spawning biomass that accounts for strain-specific reproductive efficiency. A reproductive power index (RPI) was developed for six strains of hatchery-reared lake trout using genetic data from lakes Michigan and Huron. The RPI is the ratio of the observed to expected genetic contribution of a strain to wild recruits. The Seneca Lake strain had the highest RPI, followed by Lake Manitou, Lewis Lake, Green Lake, Lake Superior, and Lake Huron strains. The RPI in southern Lake Michigan was 2.56 for Seneca Lake, 0.74 for Lake Superior, 0.50 for Lewis Lake, and 0.32 for Green Lake strains. Strain-specific effective spawning biomass in southern Lake Michigan was estimated using numbers stocked, population demographics from a stock assessment, and RPI to develop an annual effective spawning biomass index (ESBI) as a measure of reproductive potential. After 1996, ESBI increased faster than spawning biomass, and continued to increase when spawning biomass leveled off, reflecting the shift toward lake trout strains with higher RPI. The contribution to the ESBI after 2010 was 46 % Seneca Lake, 34 % wild adults, 12 % Lake Superior, and 4 % for the Lewis Lake and Green Lake strains.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".