Seasonal evolution of water contributions to discharge from a Greenland outlet glacier: insight from a new isotope-mixing model
Bibliographic record
Abstract
Abstract The Greenland ice sheet (GrIS) subglacial hydrological system may undergo a seasonal evolution, with significant geophysical and biogeochemical implications. We present results from a new isotope-mixing model to quantify the relative contributions of surface snow, glacial ice and delayed flow to the bulk meltwater discharge from a small (∼5 km 2 ) land-terminating GrIS outlet glacier during melt onset (May) and at peak melt (July). We use radioactive ( 222 Rn) and stable isotopes ( 18 O, deuterium) to differentiate the water source contributions. Atmospherically derived 7 Be further constrains meltwater transit time from the glacier surface to the ice margin. We show that (1) 222 Rn is a promising tracer for glacial waters stored at the bed and (2) a quantitative chemical mixing model can be constructed by combining 222 Rn and the stable water isotopes. Applying this model to the bulk subglacial outflow from our study area, we find a constant delayed-flow (stored) component from melt onset through peak melt. This component is diluted first by snowmelt and then by increasing glacial ice melt as the season progresses. Results from this pilot study are consistent with the hypothesis that subglacial drainage beneath land-terminating sections of the GrIS undergoes a seasonal evolution from a distributed to a channelized system.
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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 | 0.001 | 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".