Biofilm thickness restraint carriers enhance free nitrous acid inhibition for partial nitritation
Bibliographic record
Abstract
Abstract Ammonia is a deleterious pollutant present in municipal wastewater that can be hazardous if released into the environment. There is a need for the development of novel processes to advance ammonium removal technologies. Partial nitritation (PN) and anaerobic ammonia oxidation (anammox) are modern treatment processes that can be combined to provide energy-efficient ammonia removal; however, these processes have been shown to be challenging to implement as a two-stage system. New methods to achieve steady PN need to be discovered. Free nitrous acid (FNA) exposure has been explored as a possible strategy for achieving PN; however, exposure time to FNA and FNA dosage is dependent on the treatment system. For biofilm technologies, such as the moving bed biofilm reactor and biofilm characteristics, including biofilm thickness, can cause inconsistent results. Therefore, this study compares defined maximal biofilm thickness with undefined biofilm thickness and their capacity to achieve PN using FNA. This study found that a defined maximal biofilm thickness designed carrier maintained a thin biofilm capable of achieving PN after FNA exposure while the undefined biofilm thickness designed carrier was not suitable for stable PN.
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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 | 0.000 |
| 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) | 0.001 | 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".