Signatures of Selection and Genomic Diversity of Muskellunge (Esox masquinongy) from Two Populations in North America
Bibliographic record
Abstract
Muskellunge (Esox masquinongy) is the largest and most prized game fish in North America. However, little is known about Muskellunge genetic diversity in Iowa’s propagation program. We used Whole-Genome Sequencing of 12 brooding individuals from Iowa and publicly available RAD-seq of 625 individuals from the St. Lawrence River in Canada to study the genetic differences between populations, analyze signatures of selection, and evaluate the levels of genetic diversity in both populations. Given that there is no reference genome available, reads were aligned to the genome of Pike (Esox lucius). Variant calling produced 7,886,471 biallelic variants for the Iowa population and 16,867 high-quality SNPs that overlap with the Canadian samples. Principal component analysis (PCA) and Admixture analyses showed a large genetic difference between Canadian and Iowan populations. Window-based pooled heterozygosity found 6 highly heterozygous windows in the Iowa population and Fst between populations found 14 windows with fixation statistic (Fst) values larger than 0.9. Canadian inbreeding rate (Froh = 0.32) appears to be higher due to the inbreeding of Iowa population (Froh = 0.03), presumably due to isolation of subpopulations. Although inbreeding does not seem to be an immediate concern for Muskellunge in Iowa, the Canadian population seems to have a high rate of inbreeding. Finally, this approach can be used to assess the long-term viability of the current management practices of Muskellunge populations across 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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".