<i>In situ</i> measurements of sediment settling characteristics in floodplains using a LISST‐ST
Bibliographic record
Abstract
Abstract Due to a lack of data on settling velocities (ws) and grain size distributions (GSDs) in floodplain environments, sedimentation models often use calibrated rather than measured parameters. Since the characteristics of suspended matter differ from those of deposited sediment, it is impossible to derive the ws and GSD from the latter. Therefore, one needs to measure in situ suspended sediment concentrations (SSCs), settling velocities, effective grain sizes and sedimentation fluxes. For this purpose we used the LISST‐ST, a laser particle sizer combined with a settling tube. In 2002 (twice) and 2004, we located the LISST‐ST with an optical backscatter sensor and sediment traps in two floodplains in The Netherlands: one along the unembanked IJssel River, another along the embanked Waal River. Measurements revealed that the SSC in the floodplains varied in relation to the SSC in the river channel. Smaller flocs dominated the SSC, while larger flocs dominated the potential sedimentation fluxes. The in situ GSD in the IJssel floodplain was significantly coarser than in the Waal floodplain, while the dispersed median grain sizes were equal for both floodplains. Therefore, the dispersed median grain size was two to five times smaller than the effective one. The in situ grain size exhibited a significant positive relationship with ws, although the ws for the largest flocs showed high variability. Consequently, the variability in sedimentation fluxes was also large. In the actual sedimentation fluxes, and hence in sedimentation models, in situ grain sizes up to about 20 µm can be neglected. In floodplain sedimentation models the relation between settling velocity and in situ grain size can be used instead of Stokes's law, which is only valid for dispersed grain sizes. These models should also use adequate data on flow conditions as input, since these strongly influence the suspended sediment characteristics. Copyright © 2005 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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.
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