Experimental investigation of local half-cone scouring against dam
Bibliographic record
Abstract
Several methods have been proposed to control sedimentation process.These may include catchment's management, flushing, sluicing, density current venting and dredging.Flushing is used to erode previously deposited sediments.In pressurized flushing, the sediment in the vicinity of the outlet openings is scoured and a funnel shaped crater is created.In this study, the effect of bottom outlets cross section on the dimensions of flushing cone was investigated experimentally.For this purpose, experiments was carried out with three bottom outlet diameters, five discharges release for each desired depth of water and three water depths above the center of bottom outlets.The results indicate that the volume and dimensions of flushing cone are strongly affected by the bottom outlet diameter.Finally, by using regression analysis, a dimensionless equation was presented for calculating the volume of sediment released from dam and also its dimensions of half-cone scouring.
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".