Large spatial variations in the frontal mass budget of a Greenlandtidewater glacier
Bibliographic record
Abstract
Abstract. We investigate the frontal mass budget of a medium-sized tidewater glacier in western Greenland. This is done by comparing the seasonal retreat of the glacier to ice advection and ablation along the front. Frontal ablation is partitioned into calving and submarine melting, both of which are estimated from in situ observations. We observe large spatial variability in all mass budget terms along the glacier front. In particular, we find that the ablation of the glacier front is characterized by two main regimes: melting dominated versus calving dominated. While melting-dominated segments appear to be associated with subglacial discharge plumes, calving-dominated regions occur outside such plumes. The melting-dominated segments are rather localized, and the majority of ablation is estimated to occur in the form of calving. However, we stress the large uncertainty in melt rate estimates and consider the possibility that current parameterizations substantially underestimate melting. Finally, we argue that localized melt incisions into the glacier front can be significant drivers of calving. Our results suggest a complex interplay of melting and calving marked by high spatial variability along the glacier front. Understanding the impact of such local variability on larger scale ice dynamics may help guide future mass balance projections for tidewater glaciers.
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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".