Flux measurements in the Mekong river during the monsoon season
Bibliographic record
Abstract
The Mekong River is the world's 12th-longest river, with 60 million riparians and flows through six countries. Land-use changes, sediment mining and the development of dams in the Mekong and its tributaries are changing its sediment transport and morphology. Two field campaigns were organized in order to measure the flux of sediment and the spatial distribution of both particle sizes and concentration at four cross-sections located in different physiographic regions of the Lower Mekong. This communication presents the field measurement procedure, the data analysis, and the main difficulties and uncertainties to evaluate these fluxes in such large-river conditions.\nThe field procedure consists in making simultaneous measurements of flow speeds and of the spatial distribution of sediment (both suspended and bed load) using acoustic data and physical samples. Acoustic measurements are acquired with ADCPs and AQUAscat linked with GPS. Water suspended load and bedload samplings were taken with different samplers (Van Dorn , Delft bottle, Helley Smith) linked with pressure sensors to monitor the sampling depth. The concentration of sediment was determined by filtration, the grain size distributions of the sediment were measured in the field with a Laser In Situ Scattering and Transmissometry (LISST) Portable. The sand, washload and bedload fluxes were computed with these acoustic data linked with the sampling measurements. This study explains the process used to calculate these fluxes. The main uncertainty is due to subsampling and the difficulty faced measuring the sand. The results presented in this study indicate that there is a heterogeneous distribution of sand at the sites along the Lower Mekong River. The positive outcomes of this study include confirmation that the chosen techniques and instruments were appropriate for a study of this nature on rivers as large as the Mekong.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 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".