Sila qanuippa? (how's the weather?): Integrating Inuit Qaujimajatuqangit and environmental forecasting products to support travel safety around Pond Inlet, Nunavut in a changing climate
Bibliographic record
Abstract
Abstract As Inuit hunters living in Pond Inlet, Nunavut, we (Natasha Simonee and Jayko Alooloo) travel extensively on land, water, and sea ice. Climate change, including changing sea ice and increasingly unpredictable weather patterns, has made it riskier and harder for us to travel and hunt safely. Inuit knowledge supporting safe travel is also changing and shared less between generations. We increasingly use online weather, marine, and ice products to develop locally relevant forecasts. This helps us to make decisions according to wind, waves, precipitation, visibility, sea ice conditions, and floe edge location. We apply our forecasts and share them with fellow community members to support safe travel. In this paper, we share the approach we developed from over a decade of systematically and critically assessing forecasting products such as: Windy.com; weather and marine forecasts; tide tables; C-CORE’s floe edge monitoring service; SmartICE; ZoomEarth; and time lapse cameras. We describe the strengths and challenges we face when accessing, interpreting, and applying each product throughout different seasons. Our analysis highlights a disconnect between available products and local needs. This disconnect can be overcome by service providers adjusting services to include: more seasonal and real-time information, non-technical language, familiar units of measurement, data size proportional to internet access cost and speed, and clear relationships between weather/marine/ice information and safe travel. Our findings have potential relevance in the Circumpolar Arctic and beyond, wherever people combine Indigenous weather forecasting methods and online information for decision-making. We encourage service providers to improve product relevance and accessibility.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".