Estimating mountain goat abundance using DNA from fecal pellets
Bibliographic record
Abstract
Abstract Non‐invasive collection of tissue samples to obtain DNA for microsatellite genotyping required to estimate population size has been used for many wildlife species but rarely for ungulates. We estimated mountain goat ( Oreamnos americanus ) population size on a mountain complex in southwestern British Columbia by identification of individuals using DNA obtained from fecal pellet and hair samples collected during 3 sampling sessions. We identified 55 individuals from 170 samples that were successfully genotyped, and estimated a population of 77 mountain goats (SE = 7.4). Mean capture probability was 0.38 (SE = 0.037) per session. Our technique provides one of the first statistically rigorous estimates of abundance of an ungulate species using DNA derived primarily from fecal pellets. Our technique enables managers to obtain minimum counts or population estimates of ungulates in areas of low sightability that can be used for conservation and management. © 2011 The Wildlife Society.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".