A Geometric Algorithm to Identify River Meander Bends:1. Effect of Perspective
Bibliographic record
Abstract
Abstract Channels form meander bends, whether in rivers or on glaciers, volcanoes, coastlines, or the seafloor. Therefore, isolating meander bends is instrumental in characterizing channel shape and its relationship to the surrounding environment. The common approach of delimiting meander bends using inflection points yields isolated arcs that differ from traditional depictions. This study develops a geometric algorithm for mapping meander bends to bridge this gap. The approach accounts for two perceptual factors: observer viewpoint and the scale of significant deviations in the river path. The channel centerline is divided into three elements: arcs of positive/negative curvature, and effectively straight reaches with dimensionless amplitude ( A st *) below a threshold. Meander bends are formed by connecting reaches between arcs of similar curvature and trimming to where the openness, or viewshed, falls below the value for a straight line (180°). A case study for the Beatton River, Canada, shows the method captures the full extents of meander bends and reproduces a common classification (simple vs. compound) and scaling between wavelength and channel width ( 12 w c ) from visual interpretation. The number and extents of meander bends change with A st *; 0.1 < A st * < 1 prevents over‐segmentation without lumping adjacent meander bends. The approach further indicates two mapping solutions that correspond to viewpoints on opposite sides of the river. By harmonizing the geometric definition of a meander bend with its traditional depiction, this approach advances the quantitative analysis of channels across geologic environments. A companion study tests whether the mapped meander bends have characteristic shapes.
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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".