Channel Migration in Experimental River Networks Mapped by Particle Image Velocimetry
Bibliographic record
Abstract
Abstract Deltaic river networks naturally reorganize as interconnected channels move to redistribute water, sediment, and nutrients across the delta plain. Network change is documented in decades of satellite imagery and laboratory experiments, but our ability to measure and understand channel movements is limited: existing methods are difficult to employ efficiently and struggle to distinguish between gradual movements (channel migration) and abrupt shifts in river course (channel avulsions). Here, we present a method to extract channel migration from plan‐view imagery using particle image velocimetry (PIV). Although originally designed to track particles moving in a fluid, PIV can be adapted to track channels moving on the delta surface, based on input estimates of channel width, migration timescale, and maps of the wet‐dry interface. Results for a delta experiment show that PIV‐derived vector fields accurately capture channel‐bank movements, as compared to manually drawn maps and an independent image‐registration technique. Unlike other methods, PIV targets the process of channel migration, excluding changes associated with channel avulsions and overbank flow. PIV‐derived migration rates from the experiment span an order of magnitude and are reduced under lower sediment supply and during sea‐level rise, supporting recent models. Together, results indicate that PIV offers a fast and reliable way to measure channel migration in river networks, that channel migration rates under non‐cohesive conditions can displace channels a distance comparable to their width in the time needed to aggrade ∼10% of the channel depth, and that migration direction is ∼60% orthogonal to mean flow direction and ∼40% flow‐parallel overall.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".