The effects of dentate crest and a row of consecutive orifices along and below the crest edge on labyrinth weir efficiency
Bibliographic record
Abstract
A labyrinth weir is one of the most effective ways of increasing weir discharge efficiency compared with a linear overflow structure. In this study, two proposed models of labyrinth weir, one with a dentate crest and another with a row of orifices along and below the crest edge were compared with a simple labyrinth weir, measuring their effects on the discharge coefficient. Experiments on nine labyrinth weir models were conducted in the hydraulic laboratory flume. These experiments indicated that creation of both dentate crest edge and row of consecutive orifices led to an increased discharge coefficient of between 11 to 25.3% and 31.6 to 52.9%, respectively, relative to simple labyrinth weir. By increasing the upstream head water, the discharge coefficient for the labyrinth weir model with dentate crest and the model with a row of consecutive orifices are closer together and eventually will converge.
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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) | 0.000 | 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) | 0.000 | 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".