Bibliographic record
Abstract
Climate change accelerates the extensive retreat and downwasting of glaciers, leading to the widespread formations and rapid growths of glacial lakes worldwide. Spatial distribution information on glacial lakes is crucial for evidencing the climate change impacts and warning about the hazards of glacial lake outbursts. The existing glacial lake inventories are mostly region or basin specific. Therefore, there is therefore a pressing need to obtain a spatially and temporally constrained dataset of global glacial lakes. Based on a semi-automated approach, this study inventories 117,352 glacial lakes (≥0.01 km2) worldwide, with a net area of 24,755.84 ± 2,971.33 km2. The evaluation result shows that the global glacial lake data have an average overall accuracy of 89.37% and 91.42% in number and area, respectively. The global glacial lakes are widely distributed in different altitudes, ranging from the Earth’s third pole to the coastal zones. Most glacial lakes are distributed in Greenland, High-Mountain Asia (HMA), Alaska, western and northern Canada, and the cordilleras. The number and total area of glacial lakes located at the altitude below 1000 m account for 59.35% and 82.84%, respectively, whereas the lakes spanning more than 3000 m are dominantly observed in HMA. The number of glacial lakes between 0.01–0.1 km2 accounts for 77.24% of the total count but only 11.82% of the total area. The classification of glacial lakes as four groups (non–glacier-fed, ice-uncontacted proglacial, ice-contacted proglacial, and supraglacial lakes) indicates that the ice-uncontacted proglacial lakes dominate the number (67.07%) and area (53.04%) worldwide. This dataset is expected to advance the monitoring of glacial lake expansions and the assessment of glacial hazard risk.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".