Bibliographic record
Abstract
Abstract. Warming in high alpine regions is leading to an increase in glacier surface melt production, firn temperature, and firn liquid water content, altering regional hydrology and climate records contained in the ice. Here we use field observations and firn modeling to show that although the snowpack at Eclipse Icefield at 3,000 ma.s.l. in the St. Elias Range, Yukon, Canada, remains largely dry, meltwater percolation is likely to increase with an increase in extreme melt events associated with continued atmospheric warming. In particular, the development of year-round deep temperate firn at Eclipse is promoted by an increase in extreme individual melt events, rather than a greater number of small melt events or a prolonged melt season. Borehole temperatures indicate that from 2016 to 2023 there has been a 1.67 °C warming of the firn at 14 m depth (to –3.37 ± 0.01 °C in 2023). Results from the Community Firn Model show that warming of the firn below 10 m depth may continue over the next decade, with a 2 % chance of becoming temperate year-round at 15 m depth by 2033, even without continued atmospheric warming. Model results also show that the chance of Eclipse developing year-round temperate firn at 15 m depth by 2033 increases from 2 % with 0.1 °C atmospheric warming over the period 2023–2033 to 12 % with 0.2 °C warming, 51 % with 0.5 °C warming and 98 % with 1 °C warming. As the majority of the St. Elias Range’s glacierized terrain lies below Eclipse, the development of temperate firn at this elevation would represent the ability for widespread meltwater percolation in this region and a wholesale change in its hydrological system, reducing its capacity to buffer runoff and severely limiting potential ice core sites.
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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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.023 | 0.021 |
| Insufficient payload (model declined to judge) | 0.191 | 0.148 |
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".