What drives large-scale glacier detachments? Insights from Flat Creek glacier, St. Elias Mountains, Alaska
Bibliographic record
Abstract
Abstract Two large-scale glacier detachments occurred at the peaks of the 2013 and 2015 CE melt seasons, releasing a cumulative 24.4–31.3 × 106 m3 of ice and lithic material from Flat Creek glacier, St. Elias Mountains, Alaska. Both events produced highly mobile and destructive flows with runout distances of more than 11 km. Our results suggest that four main factors led to the initial detachment in 2013: abnormally high meltwater input, an easily erodible glacier bed, inefficient subglacial drainage due to a cold-ice tongue, and increased driving stresses stemming from an internal redistribution of ice after 2011. Under a drastically altered stress regime, the stability of the glacier remained sensitive to water inputs thereafter, culminating in a second detachment in 2015. The similarities with two large detachments in the Aru mountains of Tibet suggest that these detachments were caused by a common mechanism, driven by unusually high meltwater inputs. As meltwater production increases with rising temperatures, the possible increase in frequency of glacier detachments has direct implications for risk management in glaciated regions.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".