The roles of nitrogen dissimilation and assimilation in biological nitrogen removal treating low, mid, and high strength wastewater
Bibliographic record
Abstract
Nitrogen dissimilation (nitrification and denitrification) and assimilation (uptake by cell growth) under different operational conditions (chemical oxygen demand (COD) and dissolved oxygen (DO)) were evaluated in a sequencing batch reactor (SBR) system. Nitrogen dissimilation played an important role for nitrogen removal at low to mid CODs, while nitrogen assimilation became more significant with biomass concentration steadily increasing at high COD. Specific denitrification rate increased at low to mid COD (C:N < 15), but decreased at high COD (C:N > 20). Both COD and C:N ratio should be kept in proper ranges to obtain sufficient biomass concentration for nitrogen assimilation in treatment systems. With alkalinity being consumed in nitrification and produced in denitrification, effluent alkalinity indicated the corresponding nitrogen concentrations under different COD loadings. ΔAlk between influent and effluent was also well correlated with assimilation and dissimilation. The discrepancy between ΔAlkTheory and ΔAlkExperi was less than 15 mg/L when dissimilation played a predominant role in nitrogen removal, while the discrepancy increased to 30 mg/L when assimilation became dominant.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 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".