Electron Affinity Coefficients of Nitrogen Oxides and Biodegradation Kinetics in Denitrification of Contaminated Stream Water
Bibliographic record
Abstract
During the dry season in Korea, rivers become more vulnerable to contamination by biochemical oxygen demand (BOD) and nitrogen. It is hypothesized that the natural characteristics of the streams in Korea allow the contaminated water to be treated at the tributaries. Down-stream river water quality in Korea may be improved by spraying the contaminated stream water from the tributaries over the surrounding floodplains. The consequent water filtration through the soil could remove the contaminants through aerobic and denitrifying reactions. In this study, the kinetics parameters of the denitrifying reaction in floodplain filtration were determined using contaminated stream water. For the electron donor the Monod kinetics was used, while the competitive Michaelis-Menten model was employed for the electron acceptors. The parameters to the competitive Michaelis-Menten model were found using continuous denitrifying reactions, instead of the batch reactions employed in previous studies, to match the conditions needed to apply the competitive Michaelis-Menten kinetics. From the result, it was found that continuous reactions as well as batch reactions could be used to determine the affinity coefficients in denitrification. The results of this study also showed that the affinity coefficient of NO2, using continuous reactions, was similar to that of other studies in the literature found via batch reactions, whereas the affinity coefficient of N2O was much larger than that acquired with batch reactions. The parameters obtained in this study will be used in future work to simulate the contaminant behaviors during floodplain filtration using a mathematical model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".