Application of a Dynamic 2-D Mixing Model to Assess the Impact of Chemical Spills on Raw Water Quality
Bibliographic record
Abstract
A non-steady-state two-dimensional river mixing model was used in conjunction with the Hec-2 water surface profile model to develop a "spill model" by which impact of chemical spills on the raw water quality of a water treatment plant can be assessed. The dynamic 2-D mixing model was applied to the North Saskatchewan River between Devon and the Rossdale Water Treatment Plant in Edmonton, Alberta. Hec-2 compatible data were used to estimate surface water elevations for the river under varying discharge conditions. To ensure accurate estimates of the surface water elevations, Hec-2 was calibrated using the high water mark data, which were collected by the Alberta Environment during the 1986 Edmonton flood. The calibrated Hec-2 model was then used to estimate surface water elevations using flowrates from 50 m3 s1 to 5000 m3 s1. The dynamic mixing model was modified so it could utilize the cross sectional geometry data and estimated water surface elevations obtained from the Hec-2 model. Several input files were prepared for the mixing model, representing various discharge conditions of the river. A user-friendly interface was developed allowing operators easy manipulation of the mixing model. Provisions were also incorporated to allow input of required spill data such as the location of the spill, types of chemicals involved, and the magnitude of the spill. The two-dimensional mixing model estimates the concentration of a conservative chemical(s) at any point downstream of the chemical spill.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".