Incorporation of the simplified equilibrium temperature approach in a hydrodynamic and water quality model – CE-QUAL-W2
Bibliographic record
Abstract
Abstract Water temperature is an important indicator for biodiversity and ecosystem sustainability. In this study, a simplified equilibrium temperature model was incorporated into the CE-QUAL-W2 (W2) model. This model is easy to implement, needing fewer meteorological variables and no parameter calibration. The model performance was evaluated using observed data from four stations on the Lower Minnesota River. Results show that the simplified equilibrium temperature model performed as well as the original equilibrium temperature model and the term-by-term process model for water temperature predictions with the values of the coefficient of determination (R2), Nash–Sutcliffe Efficiency (NSE), and Percent Error (PE) in the accepted range (R2 = 0.974, NSE = 0.972, PE = 1.377%). The impact of the water temperature on carbonaceous biochemical oxygen demand (CBOD) concentrations under three different water temperature models was evaluated, and results show that the monthly averaged CBOD concentrations of the simplified equilibrium temperature model were almost the same as that of the term-by-term approach. For all the four calibration stations, the simplified equilibrium temperature approach performs better than the other two models for dissolved oxygen simulation (R2 = 0.791, NSE = 0.65, PE = 7.596%), which indicates that the simplified equilibrium temperature model can be a potential tool to simulate water temperature for water quality modelling.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".