Observations of Greenland Ice Sheet mass loss over the past 2ka
Bibliographic record
Abstract
This study aims to contribute data, that will improve understanding on the role of Greenland ice sheet melt in modulating midlatitude climate.A great hamper to our understanding of the influence from Greenland ice sheet melt on European climate variability comes from the lack of high-resolution observations of dynamic mass loss from the from Greenland Ice Sheet extending beyond the instrumental time scale. Building on a large repository of sediment cores taken from fjords by some of Greenland’s largest marine terminating glaciers, we aim to reconstruct multi-decadal to centennial scale changes in the iceberg production (solid ice mass loss) over the past 2ka. The IRD proxy method has conventionally been used in deep sea cores to elucidate major instability events of glacial ice sheets but has shown potential as a glacier proxy through the correspondence of the 20th century IRD records with historical and instrumental records of glacier margin positions of Sermeq Kujalleqand Upernavik Glacier in West Greenland, and Helheimand Kangerlussuaq Glaciers in Southeast Greenland.Here we show reconstructions of dynamic mass loss from from Sermeq Kujalleqand Helheim Glacier over the past 2ka. The data indicate marked melt variability at the multidecadal to centennial time scales from West Greenland during the Roman Warm Period, whereas SE Greenland Glaciers may have been buffered by sea ice at this time.
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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) | 0.001 | 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".