Prediction of Downstream Water Quality Impacts of Devils Lake Pumped Outflows Using HEC-5Q
Bibliographic record
Abstract
Since 1992 Devils Lake, a terminal lake in North Dakota, has risen nearly 7.6 meters (25 feet), flooding an additional 362 square kilometers (sq. km.) (140 square miles (sq. mi.)). In July 1999, the lake reached a peak elevation of 441.11 meters (1447.2 feet) above mean sea level, with a corresponding surface area of 518 sq. km. (200 sq. mi.). Like the Great Salt Lake in Utah, Devils Lake is highly saline due to the long-term accumulation of salts from evaporation. To alleviate flood impacts on surrounding communities, the St. Paul District, U.S. Army Corps of Engineers is investigating the feasibility and environmental impacts of pumping saline water from the lake into the fresher Sheyenne River water. The Sheyenne River flows through Lake Ashtabula, a Corps of Engineers reservoir, and into the Red River of the North, which then flows north to Lake Winnipeg in Canada, thereby creating interstate and international concerns. The Hydrologic Engineering Center's HEC-5Q program is used to address downstream water quality impacts. By simulating with- and without-pumping scenarios on the Sheyenne and Red Rivers, a quantitative evaluation of the effects can be made, including alternative operating plans for the outlet and operation of Lake Ashtabula. Vast amounts of data are conveniently handled by the HEC's Data Storage System (HECDSS). HECDSS is used to preprocess the data for input to HEC-5Q and to post-process the data from HEC-5Q for analysis.
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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 | 0.000 | 0.000 |
| 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".