A spatially constrained remote sensing-based inventory of glacial lakes worldwide
Bibliographic record
Abstract
Climate change accelerates the extensive retreat of glaciers, leading to the widespread development of glacial lakes. A holistic picture of the spatial distribution of glacial lakes worldwide is a critical base for tracking the outburst hazards. By employing a semi-automated mapping approach and rigorous quality control, this study inventories 117,352 glacial lakes (≥0.01 km 2 ) worldwide (the ice cap/sheet of Antarctic and Greenland excluded), with a net area of 24,755.84 ± 2,971.33 km 2 . The evaluation result shows this global inventory of glacial lakes (GIGLak) has an overall accuracy of 89.37% and 91.42% in number and area, respectively. These glacial lakes are widely distributed in different altitudes, ranging from the Earth’s third pole to the coasts. Most glacial lakes are distributed in the Greenland periphery, High-Mountain Asia, Alaska, Canada, and the Cordilleras. The number of glacial lakes between 0.01–0.1 km 2 accounts for 77.24% of the total count but only 11.82% in area. The classification of glacial lakes as four types indicates that the ice-uncontacted proglacial lakes dominate the number (67.07%) and area (53.04%) worldwide.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".