Efficiency of Sprayed Bentonite for Sealing of Fishpond Dams -Experimental Testing
Bibliographic record
Abstract
In this paper, the research focused on the efficiency evaluation of sprayed bentonite as a sealing layer of fishpond dams.The technology of sprayed bentonite was tested on a real scale model of an earthfill dam which was built as a 3 m wide section.The dam model was built with homogeneous earth material relatively permeable and equipped with an internal drain to monitor seepage flow.This was collected in a container equipped with an ultrasonic sensor for water level measurement purposes.The facility was also equipped with sensors for water level measurements in the reservoir upstream the dam.First, the seepage through the dam was measured without the sprayed bentonite-sealing layer.Then, the bentonite layer was sprayed on the upstream face of the dam by using a spraying technology based on the one used for dry process of sprayed concrete.This technology included a special nozzle and it was developed at Czech Technical University in Prague within the recent research.After the application of the sealing layer, the seepage through the dam was measured again and compared to the corresponding seepage without this sealing layer.The results indicate that the sealing bentonite layer lowers the seepage flow about four times compared to the solution without this sealing layer.Hence, this technology revealed efficiency with regard to the seepage flow reduction whereby will be further investigated.
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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 | 0.001 | 0.001 |
| 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 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".