Development and Application of Single‐Nucleotide Polymorphism (<scp>SNP</scp>) Genetic Markers for Conservation Monitoring of Burbot Populations
Bibliographic record
Abstract
Abstract The transboundary (Idaho, USA; and British Columbia, Canada) population of Burbot Lota lota native to the Kootenai River basin once provided a popular sport and commercial fishery and has been culturally significant to the Kootenai Tribe of Idaho for millennia. However, the population has experienced significant declines over the last 30 years, due primarily to habitat loss and alteration caused by water storage and diversion. By the late 1990s, the population was considered functionally extinct, with estimates of fewer than 50 Burbot in the wild and little to no recruitment, prompting an ongoing international recovery effort. As part of these recovery efforts, managers have been actively developing a hatchery supplementation program to rebuild the population and support future tribal subsistence harvest and recreational fisheries. Although supplementation breeding programs have the potential to rapidly rebuild depleted natural populations, careful genetic management is critical. To monitor genetic diversity and potential inbreeding in the broodstock and to provide parentage‐based tagging of supplementation offspring, we developed a set (N = 96) of highly variable single‐nucleotide polymorphism (SNP) genetic markers. The subset of 96 SNP markers was developed from a larger suite of 6,517 SNPs that were discovered by using restriction site‐associated DNA sequencing. This cost‐efficient technology allows for the rapid discovery of thousands of SNP markers in species that have not been extensively studied previously or for which there are little existing DNA sequence data. We demonstrated high accuracy (>99%) of our SNP set for parentage and individual identification through simulated and empirical tests. The SNP marker set provides a powerful new tool for managing broodstock and for monitoring and genetically tagging Burbot to track the growth, survival, and movement of released individuals.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".