Bibliographic record
Abstract
Abstract. Subglacial channels are morphologically and morphometrically distinct in comparison to fluvial channels, yet their identification from remote sensing data is still problematic. To contribute to the current set of criteria used to identify such channels, we performed detailed field observations of two subglacial channel networks on Devon Island, Nunavut, Canada. In planform, these channels are isolated, finger-like networks that drain into a main stem and have distinct cross-sectional and longitudinal profiles. Cross-sections are flat-bottomed with steep walls and longitudinal profiles are convex and exhibit undulations, typical of pressurized water flow (i.e., subglacial flow). To facilitate remote sensing identification, we interrogated how well-known scaling relationships capturing hydraulics and mass balance dynamics of fluvial systems differ in subglacial channels. Scaling relationships typically used to discern connections between discharge and channel and catchment size in fluvial systems were applied to both networks, yielding trends distinct from the fluvial literature. We suggest that the weakly correlated relationship we found between channel discharge and the size of the drainage area indicates a discrete point or line source of water, such as a moulin or crevasse.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.012 | 0.007 |
| Insufficient payload (model declined to judge) | 0.317 | 0.226 |
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".