Glaciers in Western Canada-Conterminous US and Switzerland Experience Unprecedented Mass Loss Over the Last Four Years (2021-2024)
Bibliographic record
Abstract
Over the period 2021-2024, glaciers in Western Canada and the conterminous US (WCAN-US), and Switzerland respectively lost mass at rates of 22.2 $\pm$ 9.0 and 1.5 $\pm$ 0.3 Gt yr$^{-1}$ representing a twofold increase in mass loss compared to the period 2010-2020. Since 2020, total ice volume was depleted by 12% (WCAN-US) and 13% (Switzerland). Meteorological conditions that favored high rates of mass loss included low winter snow accumulation, early-season heat waves, and prolonged warm, dry conditions. High transient snow lines, and impurity loading due to wildfires (WCAN-US) or Saharan dust (Switzerland) darkened glaciers and thereby increased mass loss via greater absorbed shortwave radiation available for melt. This ice-albedo feedback will lead to continued high rates of thinning unless recently exposed dark ice and firn at high elevations is buried by seasonal snowfall. Physical models that simulate impurity deposition and movement through firn and ice are needed to improve future projections of glacier mass 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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".