An illustrated key to the mandibles of small mammals of eastern Canada
Bibliographic record
Abstract
Skulls are often used to identify small mammals, and most identification keys to small mammals have been developed on the assumption that whole skulls will be available. However, the skulls of small mammals are seldom found intact in predator pellets or nests, and the bones of several individuals are often scattered and mixed, making counting impossible without the use of a specific cranial part. In addition, only a few keys include all the species found in the eastern provinces of Canada.Mandibles readily resist degradation by the gastric acids of both avian and mammalian predators and are often found intact in food caches of mustelids and in bat hibernacula. We therefore designed an illustrated dichotomous key to small mammals (mean mass <5 kg) of eastern Canada based on diagnostic mandible characters (including the teeth and one dentary bone). We identified and confirmed diagnostic characters to distinguish 55 species from the orders Lagomorpha, Rodentia, Soricomorpha, Carnivora, and Chiroptera. These diagnostic characters are based on a review of the literature and were confirmed by measurements performed on both museum and trapped specimens. In order to facilitate identification, photographic illustrations are provided for each couplet of the key.The ability to identify small mammals using their mandibles will reduce the number of skull components needed and has proven to be a useful tool in the study of the diet of predators. This key may also be helpful in identifying bats in the genera Myotis, Perimyotis, and Eptesicus, which are presently affected by the spread of white-nose syndrome (caused by Pseudogymnoascus destructans) throughout the eastern part of Canada.
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 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 | 0.001 | 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".