Bibliographic record
Abstract
Abstract. Snow water equivalent (SWE) retrieval from Ku-band radar measurements is possible with complex retrieval algorithms involving prior information on the snowpack microstructure and a microwave radiative transfer model to link backscatter measurements to snow properties. A key variable in a retrieval is the number of snow layers, with more complex layering yielding richer information but at increased computational cost. Here, we show the capabilities of a new method to simplify a complex multilayered snowpack to less than or equal to 3 layers, while preserving the microwave scattering behavior of the snowpack and conserving the bulk snow water equivalent. The method is based on a K-means clustering algorithm to group the snow layers based on the extinction coefficient and the height of the layer. Then, a weighted average using the extinction coefficient and the thickness was applied to the snow properties. We evaluated our method using snow properties from simulations of the SVS-2/Crocus physical snow model at 11 sites spanning a large variety of climates across the world and the Snow Microwave Radiative Transfer model to calculate backscatter at 17.25 GHz. Grouping and averaging snow stratigraphy into 3 layers effectively reproduced the total snowpack backscatter of multi-layered snowpacks with overall root mean squared error = 0.5 dB and R2 = 0.98. Using this methodology, SWE retrievals can be applied to simplified snowpacks, while maintaining similar scattering behavior, without compromising the modeled snowpack properties. Reduction in the mathematical complexity of SWE retrieval cost functions and reduction in computation of up to 80 % can be gained by using fewer layers in the SWE retrieval.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.025 | 0.013 |
| Insufficient payload (model declined to judge) | 0.375 | 0.305 |
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".