Responses of Great Horned Owls (Bubo virginianus), Barred Owls (Strix varia), and Northern Saw-whet Owls (Aegolius acadicus) to forest cover and configuration in an agricultural landscape in Alberta, Canada
Bibliographic record
Abstract
Studies on the effects of forest fragmentation on birds have focussed primarily on passerines, with few studies on owls. We assessed the influence of forest amount and configuration on the abundance and distribution of three species of forest owls, Great Horned Owl ( Bubo virginianus (Gmelin, 1788)), Barred Owl ( Strix varia Barton, 1799), and Northern Saw-whet Owl ( Aegolius acadicus (Gmelin, 1788)), in agricultural landscapes with varying amounts of forest cover in central Alberta, Canada. All three species were positively associated with forest cover: Barred Owls were most prevalent in landscapes with >66% forest cover, Great Horned Owls in landscapes with between 36% and 65% forest cover, and Northern Saw-whet Owls in landscapes with between 16% and 100% forest cover. Regression models containing configuration variables were chosen as best models using AIC for all three species. Great Horned Owls were most abundant in landscapes with high heterogeneity: more forest–nonforest edges and higher forest patch area variation. Barred Owls were more likely to occur in landscapes with larger forest patch areas and Northern Saw-whet Owls were more abundant in landscapes that were more connected. These relationships are consistent with predictions based on body size of owls and local habitat relationships described in the literature.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".