An Improved Field Method to Obtain DNA for Individual Identification From Wolf Scat
Bibliographic record
Abstract
ABSTRACT Sampling of feces for genetic studies of wild populations can be problematic because of the low quality and quantity of template DNA obtained. We used cotton swabs in the field to isolate the mucous layer on the surface of fresh wolf ( Canis lupus, C. lycaon , and their hybrids) scats followed by immediate preservation, and compared microsatellite genotyping of DNA from these fresh field swabs (FS) to that of previously frozen laboratory swabs (LS). In single polymerase chain reactions (PCRs) of 2 multiplexes, amplification at 8 loci was higher in the FS samples (FS = 50%, LS = 15%; P = 0.02) because proportion, quantity, and quality of large fragment wolf nuclear DNA from these samples was greater (2.5–25%, 6.25–62.5 ng/swab, 35% amplified at 1,000 base pairs [bp]) than from the LS samples (1.9%–10%, 4.7–25 ng/swab, 10% amplified at 1,000 bp). Paired blood and fresh field‐swabbed samples had identical genotypes. In 84 multiplex PCRs we found no evidence of allelic dropout associated with low template quality or quantity. We conclude that field swabbing of fresh wolf scat facilitates field storage and reduces the need for multiple amplifications at single microsatellite loci, thereby reducing the genotyping costs for wildlife projects that use noninvasive samples.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".