Nesting habitat selection and distribution of an avian top predator in the Canadian Arctic
Bibliographic record
Abstract
Detecting and planning for ecosystem changes from climate and land-use alteration is limited by uncertainty about the current distribution of many species. This is exacerbated in remote areas like the Arctic, where the impacts of climate change are the strongest and where industrial exploration and development are expanding. Using remotely-sensed environmental information and known nest sites, we estimated the breeding distribution and habitat selection of the peregrine falcon (Falco peregrinus) throughout most of Nunavut, a massive northern Canadian territory (>1.8 M km2) encompassing ∼15% of the world’s tundra biome. Our results show that peregrine falcons selected features of prior known importance such as rugged topography, but also sites with higher than average summer temperatures, more productive land classes, lower mean elevations, and lower mean summer precipitation. Our model identifies several areas of high relative probability of peregrine occurrence, some of which were unrecognized to date. Some of these areas may be targets for future industrial developments and are located in an area where some of the fastest climate changes are expected. Our model will allow managers to identify the areas that could be the most critical for monitoring in the context of future development and climate change.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".