A MULTI-SCALE ANALYSIS OF NORTHERN GOSHAWK HABITAT IN ONTARIO
Bibliographic record
Abstract
The northern goshawk (Accipiter gentilis) has been identified as a potential indicator of sustainable forest management by a number of state and provincial jurisdictions across North America, including Ontario. However, little is known about northern goshawk habitat use in Ontario. This thesis examined northern goshawk habitat using a multi-scale approach. Within the study, goshawk habitat was examined from the nest-tree scale through to the multiple home ranges, and even at the provincial scale. Examination of the micro-scale nesting habitat use revealed that goshawks in central Ontario preferred to nest in a hardwood tree (specifically aspen), and more importantly in trees that were significantly larger than those in the immediate vicinity. Goshawks also selected certain forest-types and avoided others. The nesting areas were characterized as well-stocked with larger (sawlog) trees and fewer small (polewood) trees, a high canopy closure, and a low shrub and ground cover. Goshawk occupancy rates did not differ for those locations with partial timber harvesting and those without. I further examined habitat use at the landscape scale. The composition and configuration of habitat in this portion of the study was examined at 7 spatial scales and multiple forest habitat classifications (including habitat defined for 3 prey species and for northern goshawks). The cumulative effect of parti al-timber harvesting only started to have a negative influence on goshawk nest occupancy at the 50-ha spatial scale, and was most influential at the 170-ha scale. Examining these multiple scales helped identify a potential threshold for goshawk occupancy of harvesting not evident at the micro-scale. I evaluated two untested goshawk habitat suitability models (HSMs) for their ability to predict known goshawk nest locations. Revisions were as analyzed and new goshawk HSM for central Ontario was iii proposed. Goshawk habitat use in Southern Ontario was also examined because the fragmented nature of this forest region compared the relatively contiguous forest cover of central Ontario examined previously in this study. Nest locations in southern Ontario still generally required continuously closed forest of 12 to 24 ha for nesting areas. However, at the broader-scale, home-range scaled buffer areas seemed to only require 20 to 30% of the area be closed forest coverage. The southern Ontario results also supported a finding from landscape analysis in central Ontario, that goshawks can tolerate a fragmented habitat, with various mean patch sizes and number of patches at the home-range scale.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".