A Note on the Discharge over Full-width Rectangular Sharp-crested Weirs and Weirs of Finite Crest Length
Bibliographic record
Abstract
The head-discharge relationship for sharp-crested weirs is developed based on the energy consideration upstream and at the crest of the weir. The discharge for weirs of finite crest length is estimated by correlating the critical depth and total energy at the upstream of the weir. In both cases, discharge is linked with the total head. Therefore, prediction of discharge for both sharp-crested weirs and weirs of finite crest length requires an iterative solution method. The present technical note formulates the relationship between the discharge coefficients based on water and total heads to estimate the error associated with implementation of head-discharge equation based on the head. The proposed prediction curves are used to convert the iterative solution method based on the total head to a direct solution strategy based on the water depth at the upstream of sharp-crested weirs and weirs of finite crest length with either a sharped-edge or rounded entrance. Based on the similarity of velocity profiles in gate flow, it is concluded that a distance as short as 2.4 times of the water head is suitable enough to measure the upstream water depth. In sharp-crested weirs, the effect of velocity head is negligible for the approach velocity ratio smaller than 0.1. Different correction curves were developed for weirs of finite crest length based on the ratio of water head to the crest length of weirs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".