Evaluating four mathematical models for nitrous oxide production by autotrophic ammonia‐oxidizing bacteria
Bibliographic record
Abstract
There is increasing evidence showing that ammonia-oxidizing bacteria (AOB) are major contributors to N(2)O emissions from wastewater treatment plants (WWTPs). Although the fundamental metabolic pathways for N(2)O production by AOB are now coming to light, the mechanisms responsible for N(2)O production by AOB in WWTP are not fully understood. Mathematical modeling provides a means for testing hypotheses related to mechanisms and triggers for N(2)O emissions in WWTP, and can then also become a tool to support the development of mitigation strategies. This study examined the ability of four mathematical model structures to describe two distinct mechanisms of N(2)O production by AOB. The production mechanisms evaluated are (1) N(2)O as the final product of nitrifier denitrification with NO(2)- as the terminal electron acceptor and (2) N(2)O as a byproduct of incomplete oxidation of hydroxylamine (NH(2)OH) to NO(2)-. The four models were compared based on their ability to predict N(2)O dynamics observed in three mixed culture studies. Short-term batch experimental data were employed to examine model assumptions related to the effects of (1) NH4+ concentration variations, (2) dissolved oxygen (DO) variations, (3) NO(2)- accumulations and (4) NH(2OH as an externally provided substrate. The modeling results demonstrate that all these models can generally describe the NH4+, NO(2)-, and NO(3)- data. However, none of these models were able to reproduce all measured N(2)O data. The results suggest that both the denitrification and NH(2)OH pathways may be involved in N(2)O production and could be kinetically linked by a competition for intracellular reducing equivalents. A unified model capturing both mechanisms and their potential interactions needs to be developed with consideration of physiological complexity.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".