Experimental Investigation of Sharp-Crested Weirs for Enhancing Water Quality in a Laboratory Flume
Bibliographic record
Abstract
Sustainable water quality is a substantial challenge for both ecological health and human consumption.Weirs are hydraulic structures widely used to regulate discharge and water levels in rivers, with their turbulence-generating capacity providing a promising method for enhancing water quality.this study aims to quantify changes in DO, TUR, TDS, pH, and EC after passage over a sharp-crested weir at different flow rates under laboratory conditions.To achieve this study, a laboratory flume with dimensions of 12 m length, 0.30 m width, and 0.46 m depth was used.The weir model dimensions were 0.25 m height, 0.29 m width, and a crest thickness of 10 mm.Measurements were taken at two points of upstream and downstream weir at a distance of 1 m under range discharges from 0.003 to 0.009 m³ /s.The tested parameters included dissolved oxygen (DO), total dissolved solids (TDS), turbidity (TUR), electrical conductivity (EC), and pH.Results showed that DO has been measured at low discharge of 6.9 to 7.3 mg/L at the upstream and downstream, respectively.At high discharge, DO was measured from 7.2 to 7.85 mg/L at the upstream and downstream, respectively.The maximum improvement was 14.7%.TDS values were slightly increased in both upstream and downstream.The maximum aeration efficiency was 15% for Q = 0.009 m 3 /s.Similarly, turbidity declined from 9.47 to 8.5 NTU at low discharge and from 9.50 to 7.9 NTU at higher discharge, with a maximum reduction of 17.2%.In contrast, pH and EC values exhibited minimal variation, remaining within stable ranges.These findings demonstrate that sharp-crested weirs can significantly enhance water quality through increased aeration and mixing, highlighting their potential as sustainable tools for ecological preservation and hydraulic management.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".