Fencerows as habitat for birds in an agricultural landscape in central Alberta, Canada
Bibliographic record
Abstract
Fencerows (strips of trees along field edges) are common in agricultural landscapes and may represent valuable habitat for forest birds in areas where woodland is scarce. We examined the relationship between avian presence (species richness, territory density, and abundance) in 26 fencerows and vegetation structure in the fencerows and forest cover in the adjacent landscape in central Alberta, Canada. Species richness was positively related to fencerow area, but not to other vegetation or landscape characteristics. In contrast, territory density was highest in smaller fencerows with high tree diversity and those with a low amount of forest cover in the surrounding landscape. Redundancy analysis indicated that abundance of 16 common species was associated with vegetation in the fencerow and/or forest cover in the surroundings. Species composition in seventeen woodlots in the area was compared with fencerow species composition. Species recorded in fencerows represented 50% of the regional species pool found in woodlots. Fencerows had mainly edge species, no interior forest species, but harbored two species (Vesper Sparrow and Eastern Kingbird) not found in woodlots. Although we advocate the retention and even restoration of fencerows, this cannot be done to the exclusion of retaining large blocks of forest in the landscape for interior forest species.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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".