Mapping and interpretation of bed-reflection power from a surge-type polythermal glacier, Yukon, Canada
Bibliographic record
Abstract
Abstract Bed-reflection power (BRP) from ice-penetrating radar has been used to make inferences about subglacial conditions and processes, yet is subject to confounding influences, including englacial attenuation and bed geometry. We use radar data collected in 2008–11 from a polythermal glacier to compute BRP with the aim of relating BRP to basal conditions. We examine the relationship between raw BRP and ice thickness, apparent bed slope and thickness of the englacial scattering layer as a proxy for internal reflection power. We then analyze a corrected form of the BRP with a graph-segmentation algorithm to delineate areas of high and low reflection power. Low corrected BRP values are found near the glacier terminus where the bed is most likely to be cold, while high corrected BRP is found in the region thought to be undergoing a slow surge. We find a spatial correlation between high BRP and high values of subglacial hydraulic upstream area, suggestive of a hydrological control on BRP. Whereas in dominantly cold glaciers BRP seems to distinguish cold from temperate regions of the bed, BRP in a polythermal glacier with a substantial volume of temperate ice may be a more complex product of thermal and hydrological conditions.
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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 | 0.002 |
| Science and technology studies | 0.001 | 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".