Ice Sheet Surface and Subsurface Melt Water Discrimination Using Multi‐Frequency Microwave Radiometry
Bibliographic record
Abstract
Abstract For understanding englacial hydrology and its impact on ice sheet mass balance, observations of the liquid water content (LWC) within the ice sheets are needed. We combined 1.4–10.7 GHz passive microwave measurements with traditional 18.7–36.5 GHz measurements to detect subsurface LWC. In situ measurements from the DYE‐2 experiment site in Greenland and a modeled LWC at this site were used to calibrate and validate the method. Our analysis showed sensitivity of the lower microwave frequencies to LWC in surface and subsurface layers down to at least 2 m, enabling detection of seasonal subsurface LWC and its refreezing. A simplified retrieval detected a delayed refreezing of subsurface LWC following surface freezing, while also capturing total seasonal meltwater production. These advancements open the door to detection of subsurface meltwater and refreezing twice a day at pan‐Greenland scale, thereby enabling improved estimates of ice sheet contributions to global sea level rise.
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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) | 0.000 | 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".