A novel model for simulation of nitrate in aquifers
Bibliographic record
Abstract
Abstract. Please Numerical groundwater quality models (GQMs) often run at high computational cost resulting in long simulation times and complex parameter calibration that limit their practical applications. In this study, a novel reduced-order model (ROM) was developed for nitrate simulation in groundwater including a simple structure and with similar accuracy as more extensive GQMs. The proposed methodology for the development of ROM presents a solution for the problem in ROMs developed with eigenvectors, to make predictions into the future. The model performance was investigated by simulation of nitrate in the Karaj Aquifer, Iran. The dominant modes of spatiotemporal variation of nitrate during a five-year period was calculated by the model. The results revealed an excellent agreement between nitrate simulated by the ROM and the well-known Modular Transport 3D Multi Species (MT3DMS). The absolute error between the ROM and the MT3DMS was less than 0.5 mg/l in the most parts of the aquifer. Thus, results confirm that the use of ROM has advantages through a much simpler structure and shorter calculation times. Observed spatiotemporal variation of nitrate in the aquifer was well represented by the ROM simulations. The simplicity of the model makes it highly interesting also to other water resources problems.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".