Modeling bioaugmentation with nitrifiers in membrane bioreactors
Bibliographic record
Abstract
Bioaugmentation with nitrifiers was studied using two pilot-scale membrane bioreactors, with the purpose of assessing the suitability of state-of-the-art activated sludge models (ASMs) in predicting the efficiency of bioaugmentation as a function of operating conditions. It was demonstrated that the temperature difference between seeding and seeded reactors (ΔT) affects bioaugmentation efficiency. Experimental data were accurately predicted when ΔT was within a range of up to 10 °C at the higher range, and when the temperature was significantly lower in the seeded reactor compared to the seeding one, standard ASMs overestimated the efficiency of bioaugmentation. A modified ASM, capable of accurately representing the behavior of seeded nitrifying biomass in the presence of high ΔT, would require the inclusion of the effect of temperature time gradients on nitrifiers. A simple linear correlation between ΔT and the Arrhenius coefficient was proposed as a preliminary step.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".