Irreversible mass loss of Canadian Arctic Archipelago glaciers
Bibliographic record
Abstract
Abstract The Canadian Arctic Archipelago (CAA) contains the largest volume of glacier ice on Earth outside of Antarctica and Greenland. In the absence of significant calving, CAA glacier mass balance is governed by the difference between surface snow accumulation and meltwater runoff—surface mass balance. Here we use a coupled atmosphere/snow model to simulate present‐day and 21st century CAA glacier surface mass balance. Through comparison with Gravity Recovery and Climate Experiment mass anomalies and in situ observations, we show that the model is capable of representing present‐day CAA glacier mass loss, as well as the dynamics of the seasonal snow cover on the CAA tundra. Next, we force this model until 2100 with a moderate climate warming scenario (AR5 RCP4.5). We show that enhanced meltwater runoff from CAA glaciers is not sufficiently compensated by increased snowfall. Extrapolation of these results toward an AR5 multimodel ensemble results in sustained 21st century CAA glacier mass loss in the vast majority (>99%) of the ~7000 temperature realizations.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".