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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it