Estimating wind slab thickness in a Tundra snowpack using Ku-band scatterometer observations
Bibliographic record
Abstract
Estimating snow water equivalent (SWE) in the northern high latitudesis important from climate, ecological and human perspectives since it enables us to track changes in spatiotemporal distribution of snow. The snow in much of this region is described as tundra, comprised of wind slab and depth hoar. Recent work in tundra environments has identified the potential of wind slab to limit radar sensitivity to SWE at 17.2 GHz, which has negative implications for SWE retrievals and demonstrates a need to constrain retrieval parameters. Radar measurements at 17.2 GHz were made in Trail Valley Creek using the University of Waterloo Scatterometer (UWScat), and combined with the Freeman-Durden polarimetric decomposition to address this need by introducing a novel relationship between wind slab thickness and double-bounce scattering, which can be used to constrain wind slab thickness. The relationship strengthens with path length through wind slab and was strong at incidence angles ≥ 46° and wind slab with thickness ≥ 19 cm. Wind slab thickness and SWE were estimated with an RMSE of 6.0 cm and 5.5 mm, respectively. This relationship is valid for use in tundra snow with depth hoar. More testing is recommended to determine the maximum detectable wind slab thickness.
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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 | 0.000 |
| 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) | 0.000 | 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 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".