The Hair Scale Identification Guide to Terrestrial Mammalian Carnivores of Canada
Bibliographic record
Abstract
Why is hair identification important? Besides just being fun and enriching to wildlife watchers, hair can serve as a passive tool to determine the presence–absence of species in a particular area (Suarez-Tangill and Rodriguez 2021). Hair can be used for many studies involving the life history of carnivores such as determining prey mammals being consumed near den sites. Hair can be easily collected and is often found where mammals are living and can be obtained as evidence in wildlife enforcement cases and to determine predation (Pasitschniak-Arts and Messier 1995). Officers and forensics specialists need to have the ability to determine identity quickly without need for DNA analysis so that hair can be properly preserved, and chain of evidence established. Despite impressions to the contrary on TV, DNA analysis is not easily and cheaply acquired for law enforcement; therefore, less expensive identification is important. Previous hair scale identification guides such as Adorjan and Kolenosky (1969) and Moore et al. (1974) are either not always accessible, especially to wildlife managers and law enforcement personnel, or are out of print and not easily obtained. The more recent key by Debelica and Thies (2009) is internet accessible but may not be easily located. A variety of hair keys have been published in scientific journals, such as Normandeau et al. (2018), but may not be accessible to conservation agency staff and enforcement officers that lack access to scientific journals. In addition, all currently published guides have geographic specificity. For example, there are guides specific to Ontario (Adorjan and Kolenosky 1969), Wyoming (Moore et al. 1974), Texas (Debelica Thies 2009), and the Rocky Mountains (Normandeau et al. 2018). This new guide by Kestler (2022) adds an accessible reference that improves upon existing publications and covers a large geographic range. This volume makes available a guide that provides color images of pelage, which is a feature absent for other hair scale keys, as well as gray-scale images of hair scales. Color range maps also help to limit the species to consider to those likely present in the area where the hair was found. The guide provides a detailed description of making hair impressions and how they are photographed. Citizen scientists and naturalists may be limited in their ability to do this because of the need for a light microscope with a camera. The other materials needed are widely available and not expensive. Mammalogists, wildlife managers, and wildlife enforcement personnel would likely have access to this equipment in a laboratory. Additional features in the book include a key to hair scales that distinguishes imbricate and coronal scales. Since all Canadian carnivore species have imbricate scales, only the five types of imbricate scales need to be considered for identification. There is also a summary of scale types (Appendix 1, p. 77) that is very useful and a series of comparisons between Felidae and Mustelidae (Appendix 2, p. 78–80). The species covered in Kestler (2022) are the seven species of Canidae, three species of Felidae, two species of Mephitidae, 10 Mustelidae, one Procyonidae, and three Ursidae. There is no distinction between Gray (Canis lupus) and Eastern Wolf (C. lycaon; Heppenheimer et al. 2018). For those in the United States, the guide does not include two Canidae, three tropical Felidae, three Mephitidae, one Mustelidae, and two Procyonidae. Each species account includes the species common name, scientific name, scale type, gross description of the pelage with a photograph, key characteristics of the scales, and names of similar species. There is, as noted above, a range map, and there are 50× and 200× photographs of hair scales. Overall, the book is concise, well-organized, and useful within the limits already noted. Its size, 80 pages, 21.6 × 14.0 cm, will fit nicely in a shoulder bag, coat pocket, or gear box to take into the field, or you can put it on a shelf in the lab near the microscope. The cover and pages are coated paper so that it should be reasonably weather-resistant or suitable for use in a lab. Considering the cost of specialized books and these features, the price is reasonable. Among those that I recommend purchase this book are mammalogists, wildlife managers, wildlife enforcement officers, wildlife forensics specialists, and anyone interested in nature and identifying carnivore hair.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.285 | 0.138 |
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".