Braiding Inuit knowledge and Western science to understand light goose population dynamics under a changing climate
Bibliographic record
Abstract
Increasing abundance of Snow and Ross’s Geese (Anser caerulescens and Anser rossii; kangut and qaaraarjuk in Inuktut, respectively), referred to collectively as light geese, has caused alterations in various Canadian Arctic ecosystems. Inuit have harvested light geese for generations and hold knowledge that offers unique insights into the ecology and population dynamics of these species. By combining interviews with 40 light goose harvesters and Elders with results from aerial surveys in the Kivalliq region of Nunavut, we (1) describe changes in light goose distribution and abundance between the 1940s and the 2010s, (2) explore the effects of light geese on local ecosystems, and (3) identify factors driving these changes. Inuit observations gathered through lifetimes of land-based observations and results from aerial surveys concurred that (1) light goose numbers have increased regionally since the 1940s, and (2) light goose numbers decreased in several colonies within the Kivalliq region between the 1960s–1990s and the 2010s, including in two Migratory Bird Sanctuaries. Inuit have noted that habitat loss due to overgrazing and grubbing has pushed light geese to abandon altered habitats in favor of new breeding and foraging sites. Inuit observations also indicated that light geese have altered their migration behavior (how, when, and where they migrate and nest) in response to earlier spring snowmelt, the drying of ponds and lakes, and an increased number of predators. These conclusions add substantially to overall understanding about light geese in regions where aerial surveys are expensive and infrequent, and scientific studies are limited in geographic coverage.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".