Snow4Flow: A new NASA airborne mission to measure and model the state and fate of Arctic glaciers
Bibliographic record
Abstract
Quantifying the ongoing retreat of glaciers and ice sheets – and projecting their futures – are major societal concerns due to their contribution to sea-level rise and influence on water resources, natural hazards, and associated socioeconomic impacts. However, our ability to confidently project glacier and ice-sheet mass change is often limited by a severe lack of observations that reliably constrain both their input (snow) and output (flow) mass fluxes. To address these needs, in April 2024 NASA selected Snow4Flow as an Earth Venture Suborbital (EVS-4) mission. Snow4Flow will capture the spatial variability in snow accumulation and ice volume across 4 Arctic and near-Arctic regions that contain hundreds of rapidly changing glaciers to deliver more reliable, societally relevant projections of land-ice change. Our target areas are Alaska and far western Canada, southeastern Greenland, the Canadian High Arctic, and Svalbard. We will perform spatially extensive multi-frequency airborne radar-sounding surveys in March–May 2027–2029, in conjunction with ground-validation campaigns. Snow4Flow will drive foundational improvements to Northern Hemisphere land-ice boundary conditions and forcing data, including orographic precipitation patterns in alpine environments, ice thickness and subglacial topography, and will directly leverage them into state-of-the-art models and projections. All associated software, datasets and model outputs will be rapidly and openly distributed to enable both independent use and assessment, along with portability to other glacierized regions on Earth.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".