Ku and X-Band Scatterometer Observations of Deep Snow at Snowex 2017: Polarimetric Responses to Microstructure Controls
Bibliographic record
Abstract
Radar measurements of accumulated snow using a Ku and X-band scatterometer instrument system were conducted at Grand Mesa, Colorado during the SnowEx 2017 field experiment in February 2017. Coincident snowpack measurements to the radar observations were made to characterize snow stratigraphy, snow density, snow grain structure, and snow thermodynamic properties. Eight sites were observed with the UWScat systems including two sites from a platform positioned 9 m above the ground and adjacent to a woodlot. The 6 non-wooded sites show consistent behavior of Ku and X-band power response (backscatter) compared with previous field studies. The first of these 6 sites was characterized by a wet snowpack response, while the other 5 had strong volume scattering response from the dry snowpack. The forest response is complex and requires further analysis to better understand the radar response from the woody biomass. The paper demonstrates and explores several response types from the radar observations of snow, including the backscatter power response, the polarimetric responses, all of which shed light on the Ku and X-band response from the ~2m snowpack.
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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 | 0.000 | 0.000 |
| 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".