Diet and reproductive success of Great Horned Owl (<i>Bubo virginianus</i>) at its northern breeding limit
Bibliographic record
Abstract
We studied the diet and reproductive success of Great Horned Owl (Bubo virginianus) at its northern range limit during an apparent high in the Snowshoe Hare (Lepus americanus) population. We performed diet analyses using images from fixed motion sensor cameras and pellet and prey remains collected at active nests, and gathered data on breeding success through camera and visual observations. Pellet data at 14 nests produced 1277 prey records consisting of 65–95% Snowshoe Hare biomass. Great Horned Owls ate 18 different prey types, with overall biomass consisting of 93% mammal, 7% bird, and less than 1% insects, frogs, and fish. The mean prey mass of 714 g (± 34 SE) was 2–25 times the mean prey mass of studies of this species at more southerly latitudes. Camera observations showed that Great Horned Owls delivered an average of 459 g/chick/d (± 75) throughout nesting. This was significantly (P = 0.005) higher than observations from Alberta, at 328–411 g/chick/d. Pellet/prey remains data showed that Great Horned Owls delivering a higher proportion of hares to their nestlings successfully raised more chicks (χ21 = 6.3, P = 0.012), highlighting the importance of this prey in the population dynamics of Great Horned Owl. In addition, we observed Snowshoe Hare removing pellets beneath nest sites, revealing an apparently undocumented bias to the use of pellet analysis.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".