Subglacial drainage patterns of Devon Island, Canada: detailed comparison of rivers and subglacial meltwater channels
Bibliographic record
Abstract
Abstract. Subglacial meltwater channels (N-channels) are attributed to erosion by meltwater in subglacial conduits. They exert a major control on meltwater accumulation at the base of ice sheets, serving as drainage pathways and modifying ice flow rates. The study of exposed relict subglacial channels offers a unique opportunity to characterize the geomorphologic fingerprint of subglacial erosion as well as study the structure and characteristics of ice sheet drainage systems. In this study we present detailed field and remote sensing observations of exposed subglacial meltwater channels in excellent preservation state on Devon Island (Canadian Arctic Archipelago). We characterize channel cross section, longitudinal profiles, and network morphologies and establish the spatial extent and distinctive characteristics of subglacial drainage systems. We use field-based GPS measurements of subglacial channel longitudinal profiles, along with stereo imagery-derived digital surface models (DSMs), and novel kinematic portable lidar data to establish a detailed characterization of subglacial channels in our field study area, including their distinction from rivers and other meltwater drainage systems. Subglacial channels typically cluster in groups of ∼10 channels and are oriented perpendicular to active or former ice margins. Although their overall direction generally follows topographic gradients, channels can be oblique to topographic gradients and have undulating longitudinal profiles. We also observe that the width of first-order tributaries is 1 to 2 orders of magnitude larger than in Devon Island river systems and approximately constant. Furthermore, our findings are consistent with theoretical expectations drawn from analyses of flow driven by gradients in effective water pressure related to variations in ice thickness. Our field and remote sensing observations represent the first high-resolution study of the subglacial geomorphology of the high Arctic, and provide quantitative and qualitative descriptions of subglacial channels that revisit well-established field identification guidelines. Distinguishing subglacial channels in topographic data is critical for understanding the emergence, geometry, and extent of channelized meltwater systems and their role in ice sheet drainage. The final aim of this study is to facilitate the identification of subglacial channel networks throughout the globe by using remote sensing techniques, which will improve the detection of these systems and help to build understanding of the underlying mechanics of subglacial channelized drainage.
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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 | 0.000 |
| 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) | 0.003 | 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".