Ice mass loss in Greenland, the Gulf of Alaska, and the Canadian Archipelago: Seasonal cycles and decadal trends
Bibliographic record
Abstract
Abstract Over the past several decades mountain glaciers and ice caps have been significant contributors to sea level rise. Here we estimate the ice mass changes in the Canadian Archipelago, the Gulf of Alaska, and Greenland since 2003 by analyzing time‐varying gravimetry data from the Gravity Recovery and Climate Experiment. Prior to 2013, interannual ice mass variability in the Gulf of Alaska and in regions around Greenland remains within the average estimated over the whole data span. Beginning in summer 2013, ice mass in regions around Greenland departs positively from its long‐term trend. Over Greenland this anomaly reached almost 500 Gt through the end of 2014. Overall, long‐term ice mass loss from Greenland and the Canadian Archipelago continues to accelerate, while losses around the Gulf of Alaska region continue but remain steady with no significant acceleration.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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".