Arctic raptor occupancy and reproductive success near a remote open-cut mine: North Baffin Island, Nunavut
Bibliographic record
Abstract
Natural resource development in the Canadian Arctic—a mostly remote and ‘untouched’ landscape—is expanding. Raptorial species are key indicators of ecosystem diversity and environmental change; disturbance-mediated changes to Arctic-breeding raptor populations can be assessed to determine impacts from development. From 2012 through 2020, we monitored peregrine falcon (Falco peregrinus) and rough-legged hawk (Buteo lagopus) breeding territories near an iron ore mine on North Baffin Island, Nunavut, Canada. The mine was constructed from 2010 through 2014 and became operational in 2015. Our objective was to evaluate whether proximity to mining disturbance affected occupancy and reproductive success of both species. We quantified occupancy using multi-season occupancy models and reproductive success using stochastic partial differential equations capable of accounting for unexplained spatiotemporal variation. Occupancy of both species was best explained by year effects. Occupancy remained relatively stable across time for peregrine falcons ( $$\lambda$$ = 0.99 ± 0.04) but fluctuated drastically for rough-legged hawks ( $$\lambda$$ = 3.41 ± 2.17). For both species, most of the spatiotemporal variation in reproductive success was unexplained (presumably from underlying abiotic and biotic factors), which led to the differential presence and count of nestlings across the study area and time. Neither distance to disturbance nor primary production explained variation in occupancy and reproductive success.
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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 | 0.001 | 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 | 0.001 | 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".