Owls of the United States and Canada: a complete guide to their biology and behavior
Bibliographic record
Abstract
There is no group of birds more mysterious and fascinating than owls. The loudmouths of the raptor world, they peep, trill, toot, bark, growl, shriek, whistle, chittle, whoop, chuckle, boom, and buzz. Indeed, very few actually hoot. They have become the stuff of lore and legend-from the Roman myth that an owl foot could reveal secrets to the First Nations belief that an owl feather could give a newborn better night vision. But the truth about owls is much more exciting. In this gorgeous book, celebrated natural history writer and wildlife photographer Wayne Lynch reveals the secrets of this elusive species with stunning photographs, personal anecdotes, and accessible science. The photos alone are masterpieces. Unlike most published owl photos, which are portraits of birds in captivity, the vast majority of these were taken in the wild-a product of the author-photographer's incredible knowledge and patience. Lynch complements the photos with a wealth of facts about anatomy, habitat, diet, and family life. For each of the nineteen species that inhabit Canada and the United States, he provides a range map and a brief discussion of its distribution, population size, and status. Lynch debunks myths about owls' supernatural powers of sight and hearing, discusses courtship rituals, and offers personal tips for finding owls in the wild. From the great horned to the tiny elf owl, this amazing volume captures the beauty and mystery of these charismatic birds of prey.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.068 | 0.032 |
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".