Automatic mapping and geomorphometry extraction technique for crevasses in geodetic mass-balance calculations at Haig Glacier, Canadian Rockies
Bibliographic record
Abstract
Abstract Finely resolved geodetic data provide an opportunity to assess the extent and morphology of crevasses and their change over time. Crevasses have the potential to bias geodetic measurements of elevation and mass change unless they are properly accounted for. We developed a framework that automatically maps and extracts crevasse geometry and masks them where they interfere with surface mass-balance assessment. Our study examines airborne light detection and ranging digital elevation models (LiDAR DEMs) from Haig Glacier, which is experiencing a transient response in its crevassed upper regions as the glacier thins, using a self-organizing map algorithm. This method successfully extracts and characterizes ~1000 crevasses, with an overall accuracy of 94%. The resulting map provides insight into stress and flow conditions. The crevasse mask also enables refined geodetic estimates of summer mass balance. From differencing of September and April LiDAR DEMs, the raw LiDAR DEM gives a 9% overestimate in the magnitude of glacier thinning over the summer: −5.48 m compared with a mean elevation change of −5.02 m when crevasses are masked out. Without identification and removal of crevasses, the LiDAR-derived summer mass balance therefore has a negative bias relative to the glaciological surface mass balance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 teacher head, 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".