Low temperature biological phosphorus removal and partial nitrification in a pilot sequencing batch reactor system
Bibliographic record
Abstract
Partial nitrification and biological phosphorus removal appear to hold promise of a cost-effective and sustainable biological nutrient removal process. Pilot sequencing batch reactors (SBRs) were operated under anaerobic/aerobic configuration for 8 months. It was found that biological phosphorus removal can be achieved in an SBR system, along with the partial nitrification process. Sufficient volatile fatty acids supply was the key for enhanced biological phosphorus removal. This experiment demonstrated that partial nitrification can be achieved even at low temperature with high dissolved oxygen (>3 mg/L) concentration. Shorter solid retention time (SRT) for nitrite oxidizing bacteria (NOB) than for ammonia oxidizing bacteria due to the nitrite substrate limitation at the beginning of the aeration cycle was the reason that caused NOB wash-out. Controlling SRT should be the strategy for an SBR operated in cold climate to achieve partial nitrification. It was also found that the aerobic phosphorus accumulating organisms' P-uptake was more sensitive to nitrite inhibition than the process of anaerobic P-release.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".