Conservation Genetics of Inland Lake Trout in the Upper Mississippi River Basin: Stocked or Native Ancestry?
Bibliographic record
Abstract
Abstract Although stocking for sport fishery enhancement has been practiced by resource managers for decades, the potential genetic effects of these stocking practices have remained largely unknown. We investigated the genetic contributions of stocking lake trout Salvelinus namaycush in two inland lakes in Wisconsin (Trout and Black Oak lakes in Vilas County), which represent the only known indigenous lake trout populations in the upper Mississippi River basin. Exogenous sources of lake trout (Lake Michigan and Lake Superior strains) have been stocked into each of these lakes for decades, although the long‐term effects of past stocking events on these populations are unknown. We used nine microsatellite loci and polymerase chain reaction– restriction fragment length polymorphism analysis of mitochondrial DNA to determine the distinctiveness and genetic ancestry of lake trout in Trout and Black Oak lakes. Measures of allelic variance indicated that Trout and Black Oak lakes were significantly different (P < 0.05) from each other (FST = 0.162) and all other populations evaluated in this study (FST = 0.101 − 0.164). The combined microsatellite and mitochondrial DNA data indicate that upper Mississippi River basin lake trout have been minimally affected by past stocking practices. These populations should be managed as native gene pools, and interlake and interbasin stocking should be avoided.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".