Bibliographic record
Abstract
This addition to the Princeton Field Guide series makes all other field guides for mammals of the United States (exclusive of Hawaii) and Canada obsolete. It was designed to enable identification of the 442 species of mammals known from North America N of Mexico. This number includes all native mammal species (both marine and terrestrial) of that region, plus exotic mammal species known to survive there and reproduce in the wild, and a few supposedly extinct or extirpated mammal species that might be found there in the future. Intended for use by professional mammalogists and amateur naturalists alike, the book provides a wealth of information in a concise volume suitable for carrying in the field. It should be noted that one of the authors of this field guide (Don Wilson) and another of his colleagues (Sue Ruff) were the editors of The Smithsonian Book of North American Mammals, which was published in 1999. That epic tome, which I reviewed earlier (Choate 2001), was a phenomenal undertaking by 229 authorities on the mammalian fauna of North America. I initially assumed that the field guide by Kays and Wilson probably was derived largely from the book by Wilson and Ruff (1999). However, I was pleasantly surprised to learn that the field guide, in several respects, improves upon its voluminous predecessor. Examples are noted below.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.017 |
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".