Evaluating the surface density and the spatial variability of the Arctic snow cover using a frequency-modulated continuous wave (FMCW) radar
Bibliographic record
Abstract
<!--!introduction!--> The arctic ecosystem is changing four times faster than the rest of the planet because of climate change. This increases the frequency of extreme weather events, like very strong winds, that have direct repercussions on snow properties, especially on its density. These kinds of events can prevent the foraging of the Peary caribou (Rangifer tarandus pearyi) as soon as the density of the snow exceeds 350 kg/m2, leading to nutritional deficiencies and even, in extreme cases, to mass mortality events due to famine. Unfortunately, the densification of the snow by strong winds is hard to study, as it is a very variable phenomenon at a local scale and the actual method of gathering density data is by digging up snow pits, which is time consuming and provides only punctual data. The usage of a frequency modulated continuous wave (FMCW) radar opens the possibility of developing an algorithm to retrieve snow density to characterize the spatial variability and impact of wind on the densification of snow cover. The elaboration of this algorithm is conducted using a portable FMCW radar that can be mounted on a sleigh or on a drone and used before and after strong winds, to map the characteristics of the snow cover’s stratigraphy. This method only requires the digital elevation model of the study site and basic knowledge of the ground vegetation, as it influences the radar backscatter. This study takes place in Cambridge Bay (Nunavut, Canada), in the Intensive Monitoring Area of the Canadian High-Arctic Research Station.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".