Pushing remote sensing capacity for climate change research in Canada’s North: POLAR’s contributions to NASA's Arctic-Boreal Vulnerability Experiment (ABoVE)
Bibliographic record
Abstract
The Arctic Boreal Vulnerability Experiment (ABoVE) is a 10-year NASA project. It assesses space-based and airborne remote sensing technologies and the way ecosystems respond to environmental change. ABoVE covers Alaska and much of northwestern Canada, from boreal forests up to the high Arctic tundra. Polar Knowledge Canada (POLAR) plays a leading role for Canada’s contributions to this mission in several ways. POLAR contributes to the science plan by performing direct research at the Canadian High Arctic Research Station in Cambridge Bay, Nunavut. It also funds external projects across Canada’s North. POLAR coordinates with the Canadian Space Agency for use of Radarsat-2 satellite imagery. NASA’s Jet Propulsion Laboratory (JPL) runs a key component of ABoVE, which is also one of NASA’s largest airborne campaigns ever. JPL flies multiple surveys across the study area using various airborne sensors. The sensors measure a range of environmental characteristics. They include: changes in water, snow, land and permafrost elevations; plant-based pigments to assess changes in vegetation; wildlife migration patterns; and greenhouse gases such as carbon dioxide and methane. While JPL’s tools are tested in the air, field researchers take similar measurements on the ground. The ground results are used to validate and calibrate those taken in the air and from space. They are also used to determine the extent of environmental change. ABoVE also has an education and outreach component, often with a focus on youth. Various communities have had information sessions, lectures, and opportunities to tour the various planes and instruments.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".