Recent, rapid and profound changes to glacier morphology and dynamics, Juneau Icefield, Alaska
Bibliographic record
Abstract
The Alaskan region (comprising glaciers in Alaska, British Columbia and Yukon) contains the third largest ice volume outside of the Greenland and Antarctic ice sheets, and contributes more to global sea level rise than any other glacierised region defined by the Randolph Glacier Inventory. However, ice loss in this area is not linear, but in part controlled by glacier hypsometry as valley and outlet glaciers are at risk of becoming detached from their accumulation areas during thinning. Plateau icefields, such as Juneau Icefield in Alaska, are very sensitive to changes in Equilibrium Line Altitude (ELA) as this can result in rapidly shrinking accumulation areas. Here, we present detailed geomorphological mapping around Juneau Icefield and use this data to reconstruct the icefield during the “Little Ice Age”. We use topographic maps, archival aerial photographs, high-resolution satellite imagery and digital elevation models to map glacier lake and glacier area and volume change from the Little Ice Age to the present day (1770, 1948, 1979, 1990, 2005, 2015 and 2019 AD). Structural glaciological mapping (1979 and 2019) highlights structural and topographic controls on non-linear glacier recession. Our data shows pronounced glacier thinning and recession in response to widespread detachment of outlet glaciers from their plateau accumulation areas. Glacier detachments became common after 2005, and occurred with increasing frequency since then. Total summed rates of area change increased eightfold from 1770-1948 (-6.14 km2 a-1) to 2015-2019 (-45.23 km2 a-1). Total rates of recession were consistent from 1770 to 1990 AD, and grew increasingly rapid after 2005, in line with regional warming.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".