The Hair Scale Identification Guide to Terrestrial Carnivores of Canada
Bibliographic record
Abstract
Mammalian predators are keystone species in any ecosystem. But many are elusive by nature and have territories that cover large areas of land, which makes them challenging to monitor. When tracks and signs prove difficult to interpret or are non-existent, hair samples recovered from the field offer a fantastic resource – one that is often overlooked. The Hair Scale Identification Guide to Terrestrial Mammalian Carnivores of Canada provides a fully illustrated, up-to-date hair scale reference for all 25 of the terrestrial carnivorous mammals of Canada. From the tiny least weasel (Mustela nivalis) to the giant polar bear (Ursus maritimus), unique traits – as well as tricky similarities – can clearly be observed through hair scale patterns magnified at the medial portion of the hair impression. These scale patterns aid in species identification when hair is the only possible evidence available. This guide also outlines hair impression techniques for samples found in the field, assisting ecologists and technicians with wildlife monitoring studies on predatory mammals where additional identification is required. Including range maps and key identification characteristics for all species represented, as well as superb images of hair scale impressions at two magnification levels, this book is a comprehensive tool for animal hair ID.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.162 | 0.080 |
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".