Video‐based gravel transport measurements with a flume mounted light table
Bibliographic record
Abstract
Abstract The study of bedload transport processes is constrained by an inability to monitor the mass, volume and grain size distribution of sediment in transport at high temporal frequencies. Building upon a previously published design, we have integrated a high‐resolution (1392 × 1024 pixels) video camera with a light table to continuously capture images of 2–181 mm material exiting a flume. The images are continuously recorded at a rate of 15 to 20 frames per second and are post‐processed using LabView(™) software, yielding continuous grain‐size‐specific transport information on a per second basis. The video capture rate is sufficient to record multiple images of each grain leaving the flume so that particle velocities can be measured automatically. No manual image processing is required. After calibration the method is accurate and precise for sediment in the 2 mm through to 45 mm grain size classes compared with other means of measuring bedload. Based on a set of validation samples, no statistically significant difference existed between the D10, D16, D25, D50, D75, D84, D90 and D95 determined by sieving captured samples and the Di values determined with the system. On average the system overpredicted transport by 4 per cent (n = 206, SD = 42%). This error can be corrected easily by simply weighing the mass of sediment that leaves the flume. The technology is relatively inexpensive and provides high‐resolution data on coarse sediment transport out of a flume. Copyright © 2008 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".