Timing Moose Pellet Collections to Increase Genotyping Success of Fecal DNA
Bibliographic record
Abstract
Abstract Fecal pellets can serve as a noninvasive source of DNA for identifying the distribution of individual animals when conducting population estimates. The quality of fecal DNA, however, can be degraded by wet or warm environmental conditions. We tested the effect of time of pellet collection (mid-March–mid-June) and temperature on the success of genotyping microsatellite loci in moose Alces alces by using DNA extracted from moose fecal pellets collected from north central British Columbia, Canada. Using the number of microsatellite loci genotyped as a measure of genotyping success clearly indicated that fecal pellets collected in March and April contained DNA that yielded high-confidence genotypes, whereas those collected in May and June did not. Pellets collected in March and April were more likely to be collected in cooler (often subzero) temperatures than those collected in May and June. Pellets collected later in the year were also more likely to be exposed to rain and increased solar radiation, all of which are likely to contribute to degradation of fecal DNA. Our findings suggest that pellets collected in late winter in the Northern Hemisphere have sufficient DNA to permit genotyping of moose.
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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 | 0.003 | 0.008 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".