Identifying guard hairs of Rocky Mountain carnivores
Bibliographic record
Abstract
ABSTRACT Increasing use of hair to survey carnivore distribution and identify carnivore scavengers or predators at prey kill sites requires methods for cost‐efficient identification of predator hair. Although DNA analysis can be used to identify species‐specific hairs, reliability depends on quality of hair, collection method, and environmental conditions, with cost that can exceed CAD$35/hair. In contrast, features of guard hairs including hair length, banding, and macro‐ and microscopic characteristics of the hair cuticle and medulla offer an alternative approach when hair quality is poor or funding is limited. Past keys focused on hair identification of prey species (e.g., ungulates, rodents) in predator scat analysis or were general because they contained all mammals in a region, thus complicating the focus on dichotomous keys for large carnivores. We used Random Forest (RF) to identify features that best classified known‐origin guard hairs ( n = 175) and used these characteristics to develop a dichotomous key for hair identification of the 7 major, large carnivore species common to the Rocky Mountains of Alberta, Canada. We found relative medulla width and pattern, cuticle‐scale characteristics, and hair length provided the greatest probability of correctly distinguishing among hairs of different carnivore species. Correct classification of within sample hairs with RF based on Area Under the Curve (AUC) averaged 0.95 ± 0.10, with coyote ( Canis latrans ) hairs having the lowest classification accuracy. Blind trials classifying 21 hairs using the dichotomous key yielded correct classifications of 88% ± 7% to the family level and 60% ± 10% to the species level. Hair preparation and identification by a trained technician was estimated at 30 ± 15 min/hair and CAD$8/hair. Our carnivore hair key provides an alternative approach to DNA hair analysis when either funds are limited, or hair samples are not of sufficient quality to be successfully sequenced. © 2018 The Wildlife Society.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".