Arctic glaciers and ground-penetrating radar. Case study: Stagnation Glacier, Bylot Island, Canada
Bibliographic record
Abstract
Introduction This case study presents results from multiple ground-penetrating radar (GPR) surveys conducted over a single ablation season at a polythermal glacier in the Canadian Arctic. Recent advances in both equipment functionality and data analysis have allowed researchers to examine notions of spatial variation in subsurface conditions, yet to date the full potential of the geophysical tool has not been exploited. A total of 30 km of radar profile data were collected illustrating how seasonal hydrothermal development can be observed in glaciers, including the appearance and disappearance of hydrological features. However, this research exemplified issues that should be borne in mind when undertaking glacier-based GPR, particularly focusing on resolution, interpolation, increasing noise and short-term temporal variability in glacier ice conditions. Over the past 50 years, radio-echo sounding (RES) and GPR have been increasingly employed on glaciers to determine a number of geometric and structural conditions. Reflection of a proportion of the impulse wave occurs where there is an abrupt, subsurface transition in dielectric constant (κ). The stark contrast between air (κ = 1), water (κ = 80), sediment (κ ≈ 25) and ice (κ ≈ 3.5) enables the reconstruction of subsurface structures from geophysical surveys. RES was initially focused upon the determination of ice thickness and thus bed topography for ice sheets (e.g. Evans 1963, Robin et al. 1969, Davis et al. 1973, Bentley et al. 1979, Hodge et al. 1990).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".