Protein to polysaccharide ratio in EPS as an indicator of non-optimized operation of tertiary nitrifying MBBR
Bibliographic record
Abstract
The protein (PN), polysaccharide (PS), and extracellular DNA (eDNA) percent concentrations of extracellular polymeric substances (EPS) of biofilm samples harvested from a pilot-scale nitrifying moving bed biofilm reactor (MBBR) were investigated at various operating temperatures and hydraulic retention times (HRTs). Chemically measured EPS PN/PS ratios were shown to correlate to Raman intensity ratios of amide III to carbohydrate at 362 rel. cm−1. The study also demonstrates that tertiary nitrifying MBBR systems may be optimized to operate at HRTs as low as 0.75 to 1.0 h as opposed to conventional HRTs of 2.0 to 6.0 h. The EPS of the nitrifying MBBR biofilm exhibited the lowest percent PN content and the highest percent PSs and eDNA content. In particular, PN/PS ratios lower than 3 were indicative of non-optimal operation of the nitrifying MBBR systems, whereas PN/PS ratios with values significantly below 3 were observed for ammonia underloaded systems at high operating temperatures and hydraulically overloaded systems at low HRTs. This study demonstrates that the PN/PS ratio in EPS is a potential metric to identify non-optimal operation of nitrifying MBBR systems.
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 | 0.000 | 0.001 |
| 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".