Estimation of Total Surface and Subsurface Meltwater Amounts Across Greenland Ice Sheet
Bibliographic record
Abstract
Greenland ice sheet (GrIS) melting has been a significant concern in the warming climate. Accurate quantification of total surface and subsurface meltwater amount across the pan-Greenland scale is crucial to understanding GrIS mass balance, thus better projecting global sea level rise. We used multi-year L-band observations from the NASA Soil Moisture Active Passive (SMAP) mission to quantify the GrIS surface and sub-surface meltwater amount and examine their spatiotemporal variability. We employed an empirical algorithm to detect surface and subsurface melt events. Then, we applied a physics-based retrieval algorithm to estimate the intensity and physical properties of the melt events. Finally, we validated the retrieval by meltwater derived from a locally calibrated energy balance model with in situ observations from the PROMICE automatic weather station (AWS) network. The retrieval and validation results are presented, which demonstrate generally a good agreement with the meltwater amounts derived from in situ observations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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".