Comparison of Ascat Estimated Snow Thickness on First-Year Sea Ice in the Canadian Arctic with Modeled and Passive Microwave Data
Bibliographic record
Abstract
The snow cover on sea ice is an important parameter controlling heat and momentum fluxes in our polar regions. Our understanding of snow thickness distributions on sea ice is severely limited by its vastness and numerous logistical difficulties. As such, we rarely collect enough in situ data from similar geographic locations to determine if and how the snow thickness distribution changes spatiotemporally. Geophysical changes in snow cover manifest as differences in dielectric properties, which are detectable in microwave emission and backscatter. Active microwave remote sensing offers improved spatial resolution when compared to passive microwave approaches. We apply our recently developed method that exploits the indirect thermodynamic control of the snow cover on near ice surface geophysical properties. The variance of C-band (5.3 GHz HH-polarization) microwave backscatter in winter (prior to melt) is assessed and is then used to estimate relative snow cover thickness and distribution. We assess the capability of our approach over landfast, first-year sea ice in the Canadian Arctic Archipelago and evaluate and compare our method against the Canadian Regional Ice Ocean Prediction system and AMSR2 passive microwave snow thickness estimates. Results demonstrate that this method can separate thick snow from thin snow on thick FYI within a thickness range of 5 to 45 cm at a spatial resolution of less than 5 km.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".