Topographic controls on ice flow and recession for Juneau Icefield (Alaska/British Columbia)
Bibliographic record
Abstract
Globally, glaciers are losing dramatic volumes of ice, especially in Alaska, which dominates sea-level rise from glaciers. Plateau icefields may be especially sensitive to climate change due to the non-linear controls their topography imparts on their response to climate change. However, Alaskan plateau icefields have been subject to little structural glaciological or regional geomorphological assessment, which makes the controls on their present and former mass balance difficult to ascertain. We inventoried 1050 glaciers and 401 lakes of the Juneau Icefield region for the year 2019. We found that 63 glaciers had disappeared since the 2005 inventory, with a reduction of glacier area of 422 km2. We also present the first structural glaciological and geomorphological map for an entire plateau icefield in Alaska. Glaciological mapping of nearly 20,000 features included crevasses, debris cover, foliation, ogives, medial moraines and, importantly, areas of glacier fragmentation, where glaciers either separated from tributaries via lateral recession (n=59), and disconnected within areas of former icefalls (n=281). Geomorphological mapping of >10,000 landforms included glacial moraines, glacial lakes, trimlines, flutes and cirques. These landforms were generated by a temperate icefield during the “Little Ice Age” neoglaciation. These data demonstrate that the present-day outlet glaciers, which have a similar thermal and ice-flow regime, have undergone largely continuous recession since the “Little Ice Age”. These data document the interactions between topography and glacier change. Importantly, disconnections are occurring within glaciers can separate accumulation and ablation zones, increasing rates of glacier mass loss. We show that glacier disconnections are widespread across the icefield and should be critically taken into consideration when icefield vulnerability to climate change is considered.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".