Effects of forest management practices on red-shouldered hawks in Ontario
Bibliographic record
Abstract
The red-shouldered hawk (Buteo lineatus) is a species of special concern throughout its northern range. It is considered to be sensitive to forest management practices because it requires dense mature hardwood forest for nesting. In Ontario, guidelines that prescribe spatial and temporal buffers were developed in about 1990 to mitigate the potential impacts of harvesting. We monitored 84 nesting areas of red-shouldered hawks in central and southeastern Ontario from 1988 to 1995 to describe the effects of forest management practices on the occupancy and productivity of nesting areas, to evaluate the effectiveness of the guidelines, and recommend modifications as appropriate. The number of years nesting areas had been used previously had a significant negative effect on activity status, but not on nest success. Nesting areas harvested with application of the guidelines had a similar probability of being active to those in uncut forest but nesting areas harvested without application of the guidelines did not. Neither the area nor proximity of selection cuts with a moderate to high residual basal area ( ≥18 m2/ha) affected the activity status of nesting areas. In contrast, the area and proximity of heavy cuts (shelterwood cuts or selection cuts with a residual basal area of 1416 m2/ha) appeared to have a negative effect on activity status. When nesting areas were active, the proximity and amount of harvesting did not influence nest success. We concluded that the impact of harvesting on the activity status of nesting areas could be mitigated by prohibiting heavy cuts within 300 m of active nests and retaining ≥20 ha of forest dominated by tolerant and mid-tolerant hardwood trees, ≥18 m tall, with ≥70% canopy closure around nests. Key words: Buteo lineatus, effectiveness monitoring, habitat guidelines, nest success, Ontario, red-shouldered hawk, forest management, selection, shelterwood, tolerant hardwoods
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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) | 0.000 | 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".