Geese as keystone species in the Low Arctic of central Canada: the Karrak Lake Research Station
Bibliographic record
Abstract
True abundance of lesser snow ( Anser caerulescens caerulescens, Linnaeus 1758) and Ross's geese ( Anser rossii, Cassin 1861, collectively referred to as “light geese”, in North America had been unknown in the 1980s. However, different indices of abundance suggested steady increases at the time. The Karrak Lake Research Station (KLRS) was established partly because it was not known if the breeding biology from more southern latitudes (the only information available at the time) was representative of more northern colonies where 90% of the midcontinent population of lesser snow geese nested. Visits in 1990 confirmed the continued importance of Karrak Lake and surrounding areas in Canada’s central Arctic to a large nesting concentration of both snow and Ross's geese documented previously. Key activities at KLRS were (1) colony-wide annual monitoring of nesting goose abundance and nesting performance and (2) large-scale marking efforts to monitor survival, range-wide abundance, distribution, and exploitation rate from hunter harvest and natural mortality rate. The scope of research expanded to include their interaction with local vegetation and other sympatric wildlife, including Arctic foxes ( Vulpes lagopus, Linnaeus 1758), king eiders ( Somateria spectabilis, Linnaeus 1758), long-tailed ducks ( Clangula hyemalis, Linnaeus 1758), and cackling geese ( Branta hutchinsii). Following exponential growth in the 1990s and 2000s, nesting populations of light geese at Karrak lake collapsed since 2012, presenting a unique opportunity to examine how local ecosystems recover and revert from heavy grazing by hyperabundant 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".