A new semi-automatic approach for dividing glacier complexes into individual glaciers
Bibliographic record
Abstract
Abstract Many glaciological and hydrological studies require outlines of individual glaciers rather than total ice cover. Here we develop a new semiautomatic algorithm that uses a digital elevation model (DEM) and outlines of glacier complexes to calculate the extents of individual glaciers. The algorithm first applies hydrological modeling tools to a modified DEM to calculate flowsheds. It then merges flowsheds that belong to individual glaciers using a distance-based approach, whose required empirical parameters are derived from the Juneau Icefield area in Alaska. In this region, 2% of ∼1300 glaciers were misclassified. The algorithm was validated on >25 000 km2 of ice in other regions in Alaska and on >40 000 km2 of ice in Arctic Canada, resulting in ∼2% and ∼3% misclassified glaciers, respectively. Results indicate that the algorithm is robust provided the DEM and the outlines are of good quality.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".