Feasibility and Recommendations for Swift Fox Fecal DNA Profiling
Bibliographic record
Abstract
Abstract: Genetic profiling using fecal samples collected noninvasively in the wild can provide managers with an alternative to live‐trapping. However, before embarking on a large‐scale survey, feasibility of this methodology should be assessed for the focal species. Costs associated with fecal genotyping can be high because of the need for multiple amplifications to prevent and detect errors. Assessing the relative amount of target DNA before genotyping means samples can be eliminated where error rates will be high or amplification success will be low, thereby reducing costs. We collected fecal samples from an endangered population of swift fox ( Vulpes velox ) and employed target‐DNA quantification and a screening protocol to assess sample quality before genetic profiling. Quantification was critical in identifying samples of low quality (68%, <0.2 ng/μL). Comparison of the amplification, from a subset of loci in 25 samples that did not meet the screening criteria, confirmed the effectiveness of this method. The protocol, however, used a considerable amount of DNA, and an assessment of the locus and sample variability allowed us to refine it for future population surveys. Although we did not use <50% of the samples we collected, the remaining samples provided 36 unique genotypes, which corresponded to approximately 70% of animals estimated to be present in the study area. Although obtaining fecal DNA from small carnivores is challenging, our protocol, including the quantification and qualification of DNA, the selection of markers, and the use of postgenotyping analyses, such as DROPOUT, CAPWIRE, and geographic information, provides a more cost‐effective way to produce reliable results. The method we have developed is an informative approach that wildlife managers can use to conduct population surveys where the collection of feces is possible without the need for live‐trapping.
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 | 0.070 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.009 | 0.003 |
| Research integrity | 0.015 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.012 |
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".