Vulnerability to geomorphological hazards of an Arctic cliff-nesting raptor, the rough-legged hawk
Bibliographic record
Abstract
Increase in temperature and precipitation associated with climate change may enhance the risk of destruction by geomorphological processes of nests or dens used by Arctic wildlife. We assessed nest vulnerability to mass movements and identified environmental factors associated with the persistence of nesting structures of rough-legged hawks (Buteo lagopus), a species that typically nests on steep slopes or cliffs. The study was conducted on Bylot Island (Nunavut) where 82 permanent hawk nesting structures, built mainly on sedimentary rocks, were monitored from 2007 to 2015. More than a quarter of known nests were destroyed during the course of the study and among those still intact, more than half were associated with a moderate to high risk of being destroyed. Nest survival analysis suggested a relatively short persistence of rough-legged hawk nesting structures on Bylot Island compared to other Arctic cliff-nesting species. Nest destruction probability increased for nests built on unconsolidated sediments, with heavy rainfall and temperature during the summer. The anticipated increase in precipitation and temperature due to climate change is likely to augment the exposure of hawk nests to mass movements, which could ultimately reduce the availability of suitable sites for the reproduction of this Arctic-nesting raptor.
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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".