Automated delineation and morphometry of unclassified subglacial bedforms.
Bibliographic record
Abstract
In the context of climate change, ice sheets are strongly influenced by the reorganization of the subglacial hydrological system and the dynamics of ice flow. Interactions between meltwater, ice flow and subglacial sediments give rise to a unique assemblage of periodic subglacial landforms composed of sediments known as bedforms. These subglacial bedforms therefore provide a large-scale observational window into the subglacial environment, which is difficult to analyze beneath current ice masses.Mapping subglacial bedforms is traditionally performed using digital elevation models (DEMs) and/or aerial or satellite imagery through manual digitization in GIS software. This method is time-consuming and introduces operator subjectivity, heavily dependent on the expertise level of the operator. This manual approach is also a significant barrier to the use of new datasets with increasingly higher resolution (e.g. ArcticDEM, RGE ALTI®, HiRISE) and coverage of ever larger areas. Addressing these limitations is essential to efficiently analyze the distribution and morphometry of subglacial bedforms over large territories.To overcome these challenges, we designed an automated tool to delineate and analyze the shape of subglacial bedforms using a recently defined land surface parameter, the Volumetric Obscurance. This parameter highlights convex and concave surfaces while minimizing the impact of noise from the topographic signal, making it particularly suited for detecting and mapping subglacial morphologies. The automated 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.Our method uses DEMs and optical satellite images, including ArcticDEM and Sentinel-2 data, to generate regional morphological maps (bedform outlines and crestlines) and regional morphometric maps (spatialized statistical analysis of bedform morphometrics). It employs a multi-threshold segmentation approach to extract bedform features and calculate both dimensional morphometric parameters (e.g., volumes, areas) and dimensionless parameters (e.g., sinuosity, circularity, elongation). These provide synthetic and spatialized information on the distribution of morphological parameters across entire bedform fields.We tested the tool on ArcticDEM data over a portion of the former Laurentide Ice Sheet bed, specifically the Keewatin Dome region in northern Canada, which displays a wide diversity of bedform shapes. The produced morphological maps demonstrated strong consistency, with approximately 75% correspondence between individual bedform outlines generated automatically and reference maps manually digitized by two distinct glacial geomorphologists. Despite a 25% difference in individual bedform outlines, the derived morphometric maps were highly comparable and provide reliable insights into subglacial deformation and hydrology.By reducing subjectivity and significantly accelerating the mapping process, this tool enables the analysis of larger areas with greater precision compared to manual methods. The derived datasets allow for reconsideration and refinement of ice-sheet scale reconstructions of ice flow and meltwater dynamics. The tool is developed in Python and is freely accessible to the research community.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".