Climate Change and Sea Ice: Local Observations from the Canadian Western Arctic” Arctic 57:1
Bibliographic record
Abstract
ABSTRACT. Can local observations and indigenous knowledge be used to provide information that complements research on climate change? Using participatory research methodology and semi-directed interviews, we explored local and traditional knowledge about changes in sea ice in the area of Sachs Harbour, Northwest Territories. In this small Inuvialuit community, we interviewed all of the 16 community members and elders considered to be local experts on sea ice to ask about their observations. We organized their comments under the headings multiyear ice, first-year ice, fractures and pressure ridges, breakup and freeze-up seasons, and other climate-related variables that influence sea ice (such as changes in winter, spring and summer temperatures, wind, rain, and thunderstorms). Observations were remarkably consistent in providing evidence of local change in such variables as multiyear ice distribution, first-year ice thickness, and ice breakup dates. The changes observed in the 1990s were said to be without precedent and outside the normal range of variation. In assessing the relevance of Inuvialuit knowledge to scientific research on climate change, we note some of the areas in which sharing of information between the two systems of knowledge may be mutually beneficial. These include the analysis of options for adapting to climate change and the generation of research questions and hypotheses for future studies. Key words: Western Arctic, climate change, Inuvialuit, traditional ecological knowledge, sea ice RÉSUMÉ. Est-ce que les observations locales et le savoir des Autochtones peuvent aider à fournir de l’information complétant la recherche sur le changement climatique? En faisant appel à une méthodologie de recherche participative et des entrevues semi-
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".