<b>Harvey, M. J., J. S. Altenbach, and T. L. Best.</b>2011 Bats of the United States and Canada. 1st ed. Johns Hopkins University Press, Baltimore, Maryland, 202 pp. ISBN-978-1-4214-0191-1, price (paper), $24.95
Bibliographic record
Abstract
This book, as the authors note, “is an updated and expanded version” of their 1999 booklet, “Bats of the United States.” Indeed, the book reviewed here is new and improved; it covers considerably more facets of bat biology, updates details of life history, occurrence, and nomenclature of the 47 species (plus 4 species of accidental occurrence), and comes in a much more convenient and durable size (think field-guide size). The first 90 or so pages include overviews of a variety of topics including classification, biology, echolocation, foraging, summer and winter habitats, hibernation, migration, reproduction and longevity, bats as food (and bombs), attracting and controlling bats, diseases, current concerns (e.g., wind power, white-nose syndrome), conservation, and status. There also are short discussions of research techniques such as inventories, thermal imaging, nets and traps, bat banding (for more information on the government bat-banding program see Ellison 2008), radiotelem-etry, and acoustic identifications. The preceding booklet covered perhaps half of these topics and not nearly so completely as is done here. These sections are amply illustrated with photographs by the authors and colleagues.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.148 | 0.124 |
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".