A semi-automated, GIS-based framework for the mapping of supraglacial hydrology
Bibliographic record
Abstract
Abstract Supraglacial drainage networks play an integral role in both glacier dynamics and run-off timing, and mapping them provides insight into their role in glacial systems. Here we present a reproducible approach for semi-automated mapping of supraglacial hydrologic features, which complements existing work in automated and manual mapping by providing clear definitions for identification of features. This framework uses a digital terrain model (DTM) to identify potential flow routes on the glacier surface, which are then classified using a set of standardized rules based on the DTM and an orthomosaic. We found that the normalized difference water index calculated from digital imagery was influenced by image brightness and introduce a new approach using average RGB values to correct for this. Using this framework we mapped supraglacial drainage networks at Nàłùdäy and Thores Glacier, Canada. The framework was easier to implement with high-resolution (0.5 m) imagery and DTMs, compared to data with lower resolution (10 m), due to the increased detail in topography and feature boundaries at high-resolution. Lower-resolution data captured larger streams (>2 pixels wide), however, indicating that the framework can still be used at this resolution. Mapping supraglacial hydrology using standardized methods opens possibilities for investigating many questions relating to changes in supraglacial hydrology over time.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".