ArcticSat: A CubeSat for Canada’s North, With Canada’s North
Bibliographic record
Abstract
Canada’s northern Inuit and Indigenous communities are experiencing climate change four times faster than the rest of the planet, impacting their traditional means of travel, hunting, and fishing. As temperatures rise, the sea ice forms later and melts earlier, allowing less time to perform these activities safely. While current satellite radiometry data can provide insight into the safety of the ice, Inuit communities require more timely and open access to this data. ArcticSat is a 3U CubeSat being co-developed with the Hamlet of Chesterfield Inlet, Nunavut, Canada, made to accomplish these goals. Community co-development ensures that community members lead the research and development of the mission, respecting their position as the rights-holders of Canada’s North. Access to ground stations installed in Canada’s Arctic will support ArcticSat’s operations and build northern satellite operations experience. The radiometer data gathered by ArcticSat will be distributed freely and quickly through Inuit data-sharing networks such as SIKU to ensure the data reaches those who need it. ArcticSat fosters collaboration throughout Canada, bolsters community co-development efforts in space, continues Canada’s remote sensing legacy, and improves community access to space.
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.045 | 0.021 |
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".