Canis STR-seq: a universal approach for non-invasive genetic monitoring of wolves and coyotes
Bibliographic record
Abstract
Population genetic studies have traditionally relied on data from short tandem repeat (STR) markers, known as microsatellites, to produce individual genotypes used in population genetics research. However, size fragment analysis from traditional capillary electrophoresis presents scoring challenges and limits data comparisons among labs. Here, we present a new, cost-effective universal microsatellite genotype-by-sequencing assay for Canis species that allows for unambiguous allele calls, flags homoplasy for more accurate assignment tests and estimates of diversity and improves genotyping output from low-template DNA. We note size homoplasy in 18 of 26 loci with the number of alleles being 32% higher in the dataset that included sequence mutations (Namut=334) compared to the dataset based on size alone (Nalen=253). Assignment tests with Bayesian cluster analysis were similar for both datasets, although 64 of 84 samples had higher assignment values to their primary cluster when mutations were considered. We document and code a list of sequence mutations associated with each locus and propose a framework for building an accessible, universal STR dataset for wolves, coyotes, and dogs that improves cluster assignments and admixture estimates in a system with complex demography and hybridization patterns. Overall, the assay provides an improved microsatellite method of genetic monitoring to aid conservation of wolf populations.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".