Distribution and Habitat Use of Ross's and Lesser Snow Geese During Late Brood Rearing
Bibliographic record
Abstract
ABSTRACT We assessed spatial distribution and habitat use by Ross's and lesser snow geese ( Chen rossii and C. caerulescens caerulescens ) during late brood rearing to begin understanding goose‐habitat interactions and monitoring key habitats around a rapidly growing nesting colony located at Karrak Lake, Nunavut, Canada. We conducted aerial surveys to count geese and georeference locations, then used Landsat Thematic Mapper satellite imagery to identify habitats associated with each flock. We observed 435 and 407 flocks and 36,287 and 32,745 birds in 1994 and 1995, respectively. Birds were somewhat uniformly distributed over the 5,000‐km 2 study area, with larger aggregations occurring closer to the coast, about 70 km from the colony. We assessed habitat use using Bonferroni intervals at both the flock and individual scales. At the flock level, birds avoided lichen‐heath, used other terrestrial habitats as available, and selected freshwater. At the individual level, geese selected lowland habitats: wet sedge meadow, hummock graminoid tundra, and freshwater, which accounted for about 70% of the birds observed, and avoided upland habitats. Selection of lowland habitats is likely due to greater availability of food and easier predator avoidance compared to drier upland areas. Because most geese in our study used freshwater habitats, our results demonstrate that assessment of carrying capacity, at least in the central Arctic, must be expanded beyond the coastal salt marshes traditionally considered by researchers and managers as primary brood‐rearing habitat for mid‐continent light geese.
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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 | 0.000 |
| 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) | 0.000 | 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".