Bibliographic record
Abstract
The best-selling field guide that "sets new standards" ( New Scientist ) and "makes all other field guides for mammals of the United States. . . and Canada obsolete" ( Journal of Mammalogy ) is now even better. Covering 20 species recognized since 2002 and including 13 new color plates, this fully revised edition of Mammals of North America illustrates all 462 known mammal species in the United States and Canada--each in beautiful color and accurate detail. With a more up-to-date species list than any other guide, improved facing-page descriptions, easier-to-read distribution maps, updated common and scientific names, and track and scat illustrations, this slim, light, and easy-to-use volume is the must-have source for identifying North American mammals. Roland Kays and Don Wilson have scoured the technical literature to pull out the key differences between similar species, and illustrated these whenever possible, making the guide useful to amateur naturalists and professional zoologists alike. Casual animal watchers will appreciate the overview of mammal diversity and the tips on identifying animals they can spy in their binoculars, while scientists will appreciate the exacting detail needed to distinguish similar species, including illustrations of shrew teeth, bat toes, and whale dorsal fins. The best-illustrated and easiest-to-use field guide to North American mammals Beautiful and accurate color illustrations of all 462 mammals found in the United States and Canada--including 20 species recognized since 2002 112 color plates--including 13 new ones Key identification information--fully revised--on facing pages The most current taxonomy/species list Fully revised, easy-to-read range maps Illustrations of tracks, scat, and whale and dolphin dive sequences
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 | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.074 | 0.027 |
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".