3D morphometry of De Geer Moraines and Crevasse-Squeeze Ridges: Differentiating between pushing and squeezing mechanisms from remotely sensed data
Bibliographic record
Abstract
De Geer Moraines (DGM) and Crevasse-Squeeze Ridges (CSR) are important landforms that can provide useful insights regarding palaeo-glacial processes. Specifically, these landforms can provide information concerning ice-marginal dynamics, and/or subglacial processes, depending on the context in which they are formed. The extraction of 3D morphometric data from these ridges can help to elucidate their formational processes, and potentially enable landform differentiation. We develop a new Python-based ArcGIS toolbox that can automatically extract 3D morphometric data from large sample sets of linear features. The morphometry toolbox may be applied to a wide range of research disciplines that are concerned with quantifying the morphometry of any elongated landforms. This is particularly useful for DGM and CSR studies, where visual similarities can result in confusion over landform type and/or formation. Here we present a case study from southwest Finland and the Northwest Territories, Canada, whereby high-resolution 3D morphometric data is used to analyse and classify DGMs and CSRs. The results reveal key differences in morphometric properties between the landforms which enables a quantified foundation by which to differentiate them. The studied CSRs are found to be higher, wider, steeper, more symmetrical, less sinuous and more voluminous than the studied prominent DGM. In contrast, a tendency for cross-sectional asymmetry in DGM supports an origin by ice-marginal pushing, rather than basal squeeze-up into crevasses. This is further supported by CSRs being less sinuous than DGM due to them being constrained to the dimensions and planform of the (relatively straight) host crevasses, whereas DGM follow a more sinuous path related to the ice margin shape. Future work should include sedimentological and geophysical studies to constrain DGM internal architecture and formation processes. The results may then be used to validate the application of DGM for detailed ice marginal reconstructions.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".