Airborne radar radiometry and coastline mapping of the highly-specular subglacial terrain on Devon island
Bibliographic record
Abstract
The highly-specular terrain present under Devon Ice Cap in the Canadian Arctic Archipelago has been the target of several multi-instrument investigation campaigns. Initial analysis of radar sounder data collected by the High Capability Radar Sounder (HiCARS) and the Multichannel Coherent Radar Depth Sounder (McCORDS) over the area using state-of-the-art quantitative methods suggested the terrain could be a hypersaline lake [Rutishauser et al., Science Advances, 2018], however, newer seismic and conductivity measurements suggest a rigid, electrically insulating material that is incompatible with liquid water [Killingbeck et al., AGU, 2022]. Starting from the hypothesis that the highly specular terrain consists of flat and smooth sediments originating from a paleolake, we propose to revisit the original radar data and to apply more advanced dielectric and subsurface rough scattering hypotheses in order to constrain the materials present in the subsurface. We also propose to use subsurface interferometric clutter discrimination [Scanlan et al., 2020, Annals of Glaciology] on Multifrequency Airborne Radar-sounder for Full-phase Assessment (MARFA) data to map the coastline of the supposed paleolake. Combining dielectric and subsurface topographic information with modeling of the thermophysical evolution of the lake over interglacial cycles could reveal the history of the formation of the structure. Preliminary work on the new radar data analysis is presented.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".