Tracing Glacial Meltwater From the Greenland Ice Sheet to the Ocean Using Gliders
Bibliographic record
Abstract
Abstract The Greenland Ice Sheet (GrIS) is experiencing significant mass loss and freshwater discharge at glacier fronts. The freshwater input from Greenland will impact the physical properties of adjacent coastal seas, including important regions of deep water formation and contribute to global sea level rise. However, the biogeochemical impact of increasing freshwater discharge from the GrIS is less well constrained. Here, we demonstrate the use of bio‐optical sensors on ocean gliders to track biogeochemical properties of meltwaters off southwest Greenland. Our results reveal that fresh, coastal waters, with an oxygen isotopic composition characteristic of glacial meltwater, are distinguished by a high optical backscatter and high levels of fluorescing dissolved organic matter (FDOM), representative of the overall colored dissolved organic matter pool. Reconstructions of geostrophic velocities are used to show that these particle and FDOM‐enriched coastal waters cross the strong boundary currents into the Labrador Sea. Meltwater input into the Labrador Sea is likely driven by mesoscale processes, such as eddy formation and local bathymetric steering, in addition to wind‐driven Ekman transport. Ocean gliders housing bio‐optical sensors can provide the high‐resolution observations of both dissolved and particulate glacially derived material that are needed to understand meltwater dispersal mechanisms and their sensitivity to future climatic change.
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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 | 0.001 | 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".