Global mapping of lake-terminating glaciers
Bibliographic record
Abstract
Abstract. Proglacial lakes at glacier termini have received widespread attention in the literature for their role in accelerating melt, velocity and contributing to cryospheric hazards. Although global and regional inventories for both glaciers and lakes exist, lake-terminating glaciers have not been consistently identified at the global scale. Based on the most recent global glacier inventory (RGI7), which so far only identifies some marine termini but none for lakes, we present a global inventory of lake-terminating glaciers, differentiating between three classes. The dataset corresponds to the year 2000 (± 1.5), matching to the timestamp of RGI7 outlines (2001, ± 6.2). We find that of 274,531 glaciers worldwide, 1.4 % terminate in lakes, varying between 0.5 and 6.7 % across 19 RGI regions. These glaciers account for 11.4 % of the total glacier area (0.2 to 41.8 % across regions). With multiple submitted flags available for 1260 individual glaciers, we find mapping conflicts to be low (6.7 %). The lake termini data set is available at https://doi.org/10.5281/zenodo.15524733 (Steiner et al., 2025) as well as at https://github.com/GLIMS-RGI/lake_terminating. This dataset is integrated into the forthcoming update to the RGI, v7.1.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".