Bibliographic record
Abstract
Abstract. Ice-marginal lakes impact glacier mass balance, water resources, and ecosystem dynamics and can produce catastrophic glacial lake outburst floods (GLOFs) via sudden drainage. Multitemporal inventories of ice-marginal lakes are a critical first step in understanding the drivers of historic change, predicting future lake evolution, and assessing GLOF hazards. Here, we use Landsat-era satellite imagery and supervised classification to semi-automatically delineate lake outlines for four ∼5-year time periods between 1984 and 2019 in Alaska and northwest Canada. Overall, ice-marginal lakes in the region have grown in total number (+183 lakes, 38 % increase) and area (+483 km2, 59 % increase) between the time periods of 1984–1988 and 2016–2019. However, changes in lake numbers and area were notably unsteady and nonuniform. We demonstrate that lake area changes are connected to dam type (moraine, bedrock, ice, or supraglacial) and topological position (proglacial, detached, unconnected, ice, or supraglacial), with important differences in lake behavior between the sub-groups. In strong contrast to all other dam types, ice-dammed lakes decreased in number (six fewer, 9 % decrease) and area (−51 km2, 40 % decrease), while moraine-dammed lakes increased (56 more, 26 % and +479 km2, 87 % increase for number and area, respectively) at a faster rate than the average when considering all dam types together. Proglacial lakes experienced the largest area changes and rate of change out of any lake position throughout the period of study and moraine-dammed lakes which experienced the largest increases are associated with clean-ice glaciers (<19 % debris cover). By tracking individual lakes through time and categorizing lakes by dam type, subregion, and topological position, we are able to parse trends that would otherwise be aliased if these characteristics were not considered. This work highlights the importance of such lake characterization when performing ice-marginal lake inventories and provides insight into the physical processes driving recent ice-marginal lake evolution.
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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 | 0.028 |
| 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 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.019 | 0.017 |
| Insufficient payload (model declined to judge) | 0.229 | 0.162 |
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".