Development of a Noninvasive Genotyping‐In‐Thousands (<scp>GTseq</scp>) Panel for Long‐Term Conservation of Western Great Lakes Gray Wolves (<i>Canis lupus</i>)
Bibliographic record
Abstract
ABSTRACT The application of noninvasive genetic methods toward the field of conservation has increased our understanding of many wildlife populations that are difficult to sample, allowing for better management. In molecular ecology, the use of noninvasive sampling became widely feasible with the advent of microsatellites, a highly polymorphic, short‐length marker that could be genotyped from low‐quality DNA sources. Despite decades of use, many microsatellite panels continue to suffer from high genotyping error rates, allelic dropout, and limited reproducibility across laboratories. To address these issues, single nucleotide polymorphisms (SNPs) offer advantages such as lower genotyping error rates, avoidance of allelic dropout due to consistent allele length, and automated calling through bioinformatic pipelines, reducing human subjectivity and error. Given the advantages SNPs provide relative to microsatellites as a molecular marker, the use of SNP panels and specifically, the method of genotyping‐in‐thousands by sequencing (GTseq) has gained popularity. Here, we developed a GTseq panel for western Great Lakes canids comprised of 196 loci, capable of species identification, accurately inferring sex (97.2%), identifying unique individuals (probability of identity = 6.71e−41), assigning relationships (false positive rate = 9.34e−14), and assigning genotypes with low error (0.39%). In an attempt to improve genotyping success with low‐quality samples, we found that while increasing the number of PCR cycles yielded a higher percentage of genotyped loci, it also increased on‐target reads in negative PCR controls. We suggest approaching this manipulation with caution and emphasize the importance of including and reporting negative PCR controls. Further, quantitative PCR was a powerful method to estimate host‐specific DNA concentrations, enabling conservative sample selection for library preparation with respect to GTseq affordability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".