The geometry and complexity of spatial patterns of terrestrial channel networks: Distinctive fingerprints of erosional regimes
Bibliographic record
Abstract
The morphology of channel networks related to long‐term erosion reflects the mechanisms involved in their formation. This study aims to identify quantitative metrics, drawn from topographic data and satellite imagery, that are diagnostic of the distinctive styles of erosion by rivers, glaciers, subglacial meltwater, and groundwater sapping. From digital elevation models, we identify three geometric metrics: the minimum channel width, channel aspect ratio (longest length to channel width at the outlet), and tributary junction angle. We also characterize channel network complexity in terms of its stream order and fractal dimension. To validate our approach, we perform a principal component analysis (PCA) on measurements of these five metrics on 70 channel networks. We build understanding of these results, in turn using scaling analyses of appropriate physical models. We show that rivers, glaciers, and groundwater sapping erode the landscape in rigorously distinguishable ways. Whereas rivers are characterized by nearly constant minimum width, variable aspect ratio, and high stream orders, glaciers have highly variable minimum widths and aspect ratios and much smaller stream orders. Erosion by subglacial meltwater remains poorly understood, and we argue that we require an additional metric to fully characterize these systems. Our methodology can more generally be applied to identify the contributions of different processes involved in carving a channel network. In particular, we are able to identify transitions from fluvial to glaciated landscapes or vice versa.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".