High curvatures drive river meandering
Bibliographic record
Abstract
One of the long- and widely held ideas about the dynamics of meanderingrivers is that migration slows down in bends with higher curvatures.Identifying the radius of curvature at which migration is fastest isstandard practice in field studies of meandering rivers. High-resolutionmeasurements of local migration rates in time-lapse Landsat images fromtwo rapidly migrating rivers in the Amazon Basin suggest that thevariation of migration rate closely follows that of the local curvature,with a roughly constant phase lag between the two; and a quasi-linearrelationship exists between curvature and migration rate if this lag istaken into account. A simple numerical model of meandering illustratesthe link between curvature and migration rate and reproducesobservations from the studied rivers. The implication is that meanderingrivers migrate fastest at, and slightly downstream of, locations ofhigh curvature; and one of the most important ways river migration isrejuvenated and meandering patterns are reshuffled is the generation ofhigh-curvature bends through cutoffs.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".