Video‐based gravel transport measurements with a flume mounted light table
Why this work is in the frame
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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 D 10 , D 16 , D 25 , D 50 , D 75 , D 84 , D 90 and D 95 determined by sieving captured samples and the D i 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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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it