The effects of survey frequency on estimates of scour and fill in a braided river model
Bibliographic record
Abstract
Abstract An erratum has been published for this article in Earth Surface Processes and Landforms 27(7) 2002, 795. Estimates of scour and fill in rivers that are derived by differencing topographic surfaces are known to be negatively biased by local compensation of scour and fill between surveys but the magnitude of bias is not well known. This study examines the effect of survey frequency on volumes of scour and fill over a period of active channel braiding in a small‐scale river model. A 100 min, high temporal resolution time series of digital elevation models is artificially coarsened by selectively removing models. The resulting four overlapping time series have survey intervals of 10 min, 20 min, 50 min and 100 min. Cumulative scour and fill volumes for the 100 min period are compared between the four series. It is concluded that the decay in measured volumes of scour and fill with increased survey interval can be described using inverse functions. Cumulative scour–fill volumes are approximately 420 per cent greater over the study period for 10 min survey intervals than for a 100 min interval. After the 100 min period of competent flow, nearly 65 per cent of the channel area experienced significant compensation of scour and fill. Several compensation mechanisms were identified in association with braided channel kinetics, including lateral channel migration, the migration of bed forms, and channel avulsion. It is demonstrated that by negatively biasing scour, fill and net estimates, this error significantly affects morphological approaches to the estimation of bed load sediment transport. Copyright © 2002 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.010 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".