Upscaling ground‐based structural glaciological investigations via satellite remote sensing to larger‐scale ice masses: Bylot Island, Canadian Arctic
Bibliographic record
Abstract
Abstract Using satellite remote sensing, this study aims to assess the validity of upscaling ground‐based structural observations of small valley glaciers, to larger‐scale ice masses that are too vast or inaccessible for field‐study or ground‐truthing. Focusing on four adjacent valley glaciers on Bylot Island, Nunavut, Arctic Canada, we establish that ground‐based structural observations from two smaller (Stagnation and Fountain Glaciers) can be used to interpret the structures visible in optical satellite imagery in two much larger glaciers (Aktineq and Sermilik Glaciers). All the glaciers investigated have prominent longitudinal lineations, which are interpreted from ground observations to be longitudinal foliation. Other structures that were identified include primary stratification, crevasses, crevasse traces, and thrust‐faults. Strong longitudinal foliation is concentrated at flow‐unit boundaries, with differential ablation of ice facies commonly resulting in a ridge‐and‐furrow supraglacial topography that controls supraglacial streams and debris concentrations. Consequently, areas of strong foliation appear darker than areas of weak foliation in satellite imagery. As coarser resolution imagery is utilized to map large‐scale ice masses, sub‐pixel structural information is lost. Individual lineations mapped in coarser resolution imagery therefore probably comprise groups of clustered foliation at the sub‐pixel scale. Lateral narrowing measurements and calculated one‐dimensional strain across zones of longitudinal foliation are assessed as a tool for identifying large‐scale surface strain patterns, in particular large‐scale pure shear regimes. These one‐dimensional strain measurements suggest that flow‐unit boundaries are areas that undergo considerable cumulative strains. The upscaling approach used here can be applied to the largest ice masses, notably the Antarctic Ice Sheet.
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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 | 0.001 |
| Science and technology studies | 0.002 | 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".