Topographic controls on ice flow and recession for Juneau Icefield (Alaska/British Columbia)
Bibliographic record
Abstract
Abstract Globally, mountain glaciers and ice caps are losing dramatic volumes of ice. The resultant sea‐level rise is dominated by contributions from Alaska. 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 368 lakes in the Juneau Icefield region for the year 2019. We found that 63 glaciers had disappeared since the 2005 inventory, with a reduction in glacier area of 422 km 2 (10.0%). We also present the first structural glaciological and geomorphological map for an entire icefield in Alaska. Glaciological mapping of >20 800 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), or disconnected within areas of former icefalls ( n = 281). Geomorphological mapping of >10 200 landforms included glacial moraines, glacial lakes, trimlines, flutes and cirques. These landforms were generated by a temperate icefield during the Little Ice Age (LIA) 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 LIA. Importantly, disconnections 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".