Development of an Ungulate Mammalian Hair Key
Bibliographic record
Abstract
We have created a dichotomous hair key to differentiate between adult and juvenile ungulates seen in summer wolf diets; however we would like to stress that this key can also be used for other large carnivore diets, as it has shown merit in the identification of cougar and coyote prey items. To our knowledge there is no mammalian hair key that is able to sufficiently differentiate between juvenile and adults of the major species in the North American Wolf’s (Canis lupus) diet, specifically, mule deer, white-tailed deer, elk, moose and bighorn sheep. We acquired juvenile hair samples starting at birth at bi-weekly to monthly intervals from various zoos, wildlife parks and rehabilitation centres over North America for all the major prey species except bighorn sheep. Main characters used to classify ungulate hair were basal scale margin distance, hair diameter and hair color. Scale margin distance and basal hair diameter was measured via a microscope ocular micrometer, with t-tests completed to assess differences between species. We were successful at differentiating between all juvenile ungulates, however juvenile deer species hair maybe very difficult to differentiate between and may only be accurately done with experience. Juvenile ungulate hair is usually smaller in diam- eter then the guard hair of adults and is more delicate in appearance. As observed in other study by De Marinis et al 2006, we too were able to differentiate juvenile ungulates by a scalloped medulla (Figure 1). We were able to distinguish juvenile moose by their ginger color appearance, large medial hair cuticle scales, and large basal hair diameter compared to other ungulate species (Figure 2). We also found the juvenile elk had smaller hair diameters then juvenile deer and appear less ridged compared to juvenile deer.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".