Six Decades of Glacial Mass Loss in the Canadian Arctic Archipelago
Bibliographic record
Abstract
The Canadian Arctic Archipelago comprises multiple small glaciers and ice caps, mostly concentrated on Ellesmere and Baffin Islands in the northern (NCAA, Northern Canadian Arctic Archipelago) and southern parts (SCAA, Southern Canadian Arctic Archipelago) of the archipelago, respectively. Because these glaciers are small and show complex geometries, current regional climate models, using 5‐ to 20‐km horizontal resolution, do not properly resolve surface mass balance patterns. Here we present a 58‐year (1958–2015) reconstruction of daily surface mass balance of the Canadian Arctic Archipelago, statistically downscaled to 1 km from the output of the regional climate model RACMO2.3 at 11 km. By correcting for biases in elevation and ice albedo, the downscaling method significantly improves runoff estimates over narrow outlet glaciers and isolated ice fields. Since the last two decades, NCAA and SCAA glaciers have experienced warmer conditions (+1.1°C) resulting in continued mass loss of 28.2 ± 11.5 and 22.0 ± 4.5 Gt/year, respectively, more than doubling (11.9 Gt/year) and doubling (11.9 Gt/year) the pre‐1996 average. While the interior of NCAA ice caps can still buffer most of the additional melt, the lack of a perennial firn area over low‐lying SCAA glaciers has caused uninterrupted mass loss since the 1980s. In the absence of significant refreezing capacity, this indicates inevitable disappearance of these highly sensitive glaciers.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".