Automated delineation and morphometry of unclassified subglacial bedforms
Bibliographic record
Abstract
We designed an automated tool to delineate and analyse the shape of subglacial bedforms using a recently defined land surface parameter, the volumetric obscurance. The tool is based on the assumption that the diversity of subglacial bedform shapes reflects a continuum; therefore, unlike traditional methods, no pre‐ or post‐mapping classification of bedforms is performed. It uses digital elevation models and optical satellite images to generate regional morphological maps (bedform outlines and crestlines) and regional morphometric maps (spatialized statistical analysis of bedform morphometrics). We tested the tool on the ArcticDEM, over a portion of the former Laurentide Ice Sheet bed that displays a wide diversity of bedform shapes (Keewatin Ice Dome, northern Canada). The produced morphological maps are consistent, with a correspondence of approximately 75% on individual bedform outlines, with two reference maps digitized manually by two different glacial geomorphologists. Despite the 25% difference between individual bedform outlines generated automatically and manually, the derived morphometric maps are similar. They can be interpreted in the context of subglacial deformation and hydrology, providing a new potential for palaeoglaciological reconstructions at the ice‐sheet scale. The tool was developed in Python and is freely accessible.
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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) | 0.000 | 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".