Case Study: Intermediate Field Mixing for a Bank Discharge in a Natural River
Bibliographic record
Abstract
The intermediate field mixing characteristics of the Gold Bar Wastewater Treatment Plant effluent into the North Saskatchewan River at Edmonton were evaluated. This region may be considered to be the early part of the transverse mixing region where local channel characteristics are important. An extensive field study was conducted to delineate the bathymetry of the study area and evaluate the mixing characteristics by means of a steady state dye test. The topographic and limited velocity results of the field study were used to create and validate a depth-averaged hydrodynamic model of the study reach in order to extract streamtube information. The results from the hydrodynamic model were used to interpret the mixing characteristics of the study reach as well as extract channel characteristics. From the analysis it is evident that the distribution of effective transverse mixing coefficient is highly dependent on local river conditions. The use of the hydrodynamic model to extract channel characteristics provided a reasonable estimate of mixing characteristics without requiring detailed field velocity data. The trade-off is more detailed bathymetry data is required to have a realistic model. Plume averaged channel characteristics rather than cross sectional averaged were shown to produce more realistic transverse mixing coefficients. Assumed Gaussian profile distributions were successfully applied suggesting that for a bank discharge if the maximum bank concentration and mass flux are known this technique could be applied.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".