Planform‐asymmetry and backwater effects on river‐cutoff kinematics and clustering
Bibliographic record
Abstract
Abstract River bends occasionally meander to the point of cutoff, whereby a river shortcuts itself and isolates a portion of its course. This fundamental process fingerprints a river's long‐term planform geometry, its stratigraphic record, and biogeochemical fluxes in the floodplain. Although meander cutoffs are common in fast‐migrating channels, timelapse imagery of the Earth surface typically does not offer a long enough baseline for statistically robust analyses of these processes. We seek to bridge this gap by quantifying cutoff kinematics along the Humboldt River (Nevada) – a stream that, from 1994 to 2019, hosted an exceptionally high number of cutoffs (specifically, 174 of the chute type and 53 of the neck type). A coincidence between major floods and cutoff incidence is first suggestive of hydrographic modulation. Moreover, not just higher sinuosity but also upstream planform skewness is associated with higher cutoff incidence and channel widening for a sub‐population of chute cutoffs. We propose a conceptual model to explain our results in terms of channel‐flow structure and then examine the distances between adjacent cutoffs to understand the mechanisms governing their clustering. We find that both local and nonlocal perturbations together trigger the clustering of new cutoffs, over distances capped by the backwater length and over yearly to decadal timescales. Our research suggests that planform geometry and backwater controls might sway the occurrence of cutoff clusters – both local and nonlocal – thereby offering new testable hypotheses to explore the evolution of meandering‐river landscapes that have significant implications for river engineering and stratigraphic modelling. © 2020 John Wiley & Sons, Ltd.
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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) | 0.000 | 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".