A Geometric Algorithm to Identify River Meander Bends: 2. Test for Characteristic Shapes
Bibliographic record
Abstract
Abstract River meander bends are widely considered to have recurring shapes. Formal classifications of meander bends have provided an important framework for basic research and practical applications in river engineering and restoration. However, the central role of expert interpretation in both mapping and classifying meander bends leaves persistent uncertainty for whether their shapes do form patterns or instead represent a continuum of forms. This study analyzes meander bend shapes derived in a companion paper about the Beatton River, Canada, to test whether meander bends show repeating shapes without the prior assumption that such patterns exist. Meander bends are compared using Fréchet distance, a curve similarity measure, and evaluated for common shapes using agglomerative hierarchical clustering. The case study indicates that normalizing meander bend coordinates by wavelength and amplitude yields clusters of meander bends with internally consistent shapes. Characteristic meander bends for each cluster are derived by averaging the normalized coordinates and rescaling by the mean amplitude and wavelength for the source meander bends. Whereas automatically mapped meander bends vary in number and extent with a dimensionless amplitude threshold ( A st *), the characteristic meander bends are generally robust against variation in this parameter within its effective range (0.1 A st * 1). By establishing a test for characteristic shapes among populations of multifarious meander bends, the analysis enables new tests for environmental controls on channel form, a standard for assessing the fidelity of numerical models for planform evolution, and a method to design nature‐based templates for river restoration and engineering.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".