Bibliographic record
Abstract
Abstract. Snow is an important freshwater resource that impacts the health and well-being of communities, the economy, and sustains ecosystems of the cryosphere. This is why there is a need for a spaceborne Earth observation mission to monitor global snow conditions. Environment and Climate Change Canada, in partnership with the Canadian Space Agency, is developing a new Ku-band synthetic aperture radar mission to retrieve snow water equivalent (SWE) at a nominal resolution of 500 m, and weekly coverage of the cryosphere. Here, we present the concept of the SWE retrieval algorithm for this proposed satellite mission. It is shown that by combining a priori knowledge of snow conditions from a land surface model, like the Canadian Soil Vegetation Snow version 2 model (SVS-2), in a Bayesian model, we can retrieve SWE with an RMSE of 15.8 mm (16.4 %) with an uncertainty of 34.8 mm (37.7 %). To achieve this accuracy, a larger uncertainty in the a priori grain size estimation is required, since this variable is known to be underestimated within SVS-2 and has a considerable impact on the microwave scattering properties of snow. It is also shown that adding four observations from different incidence angles improves the accuracy of the SWE retrieval because these observations are sensitive to different scattering mechanisms of the snowpack. These results validate the mission concept of the proposed Canadian satellite mission.
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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 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.024 | 0.019 |
| Insufficient payload (model declined to judge) | 0.117 | 0.090 |
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".