Hydraulic modelling for assessment of the performance of sedimentation basins downstream from extracted peatlands
Bibliographic record
Abstract
Three sedimentation basins on two different extracted peatlands were studied to determine their Trapping Efficiency (TE) using two different methods. First, TE was calculated using sediment loads estimated from turbidity measurements upstream and downstream of the basins. The second method was based on hydraulic modelling and a simplified sediment deposition model. For the first studied basin (controlled by a weir at its downstream end) TE was estimated with the second method at 85.9 % and 55.6 % for lower and higher flows, respectively. In the second peatland the studied basins were in series, there was a geotextile curtain in the middle of each basin and a weir or a double pipe culvert at the outlet. For these two basins in series, TE was estimated at 80 % for lower flows and at 34.3 % for higher flows. A hydraulic model was calibrated for the studied basins and applied to estimate the TE of different basin configurations. The results show that the role of the geotextile curtain is important in the case of short basins and for intense rainfall events. The double pipe culvert did not have a significant effect on TE, unlike the presence of a weir at the outlet, which is required to maintain high TE.
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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) | 0.001 | 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".