Detecting structural and functional differences in activated sludge bacterial communities originating from laboratory treatment of elementally and totally chlorine-free bleaching effluents
Bibliographic record
Abstract
The ability to differentiate functional and structural diversity of bacterial communities present in activated sludges adapted to elementally (ECF) and totally (TCF) chlorine-free bleaching effluents was evaluated. Community function was evaluated through substrate utilization patterns in BiologGN microplates, and taxonomic structure was evaluated by fluorescent in situ hybridization using probes targeting the Eubacteria; the alpha, beta, and gamma subclasses of the Proteobacteria; and gram-positive bacteria with high GC content. Over 6-week sampling periods, ECF-and TCF-adapted sludge bacterial communities presented reproducible substrate utilization patterns that through principal components (PCs) analysis, separated the ECF samples from the TCF samples. Application of the fluorescent in situ hybridization technique was complicated by the intense autofluorescence of the bleaching effluent sludge samples that interfered with detection of specific hybridization signals. The most notable difference in community structure detected using the chosen set of probes was the relatively greater proportion of cells of the alpha subclass in TCF sludge (27%) than in ECF sludge (6%). Nonspecific hybridization with beta and gamma probes was relatively high, but both sludges appeared to have similar proportions of cells of the beta (20-22%) and gamma (11-12%) subclasses. The two sludges presented relatively few gram-positive cells with high GC content (<0.2% of eubacterial counts). Differences in both metabolic potential and taxonomic structure of the microbial communities in the ECF- and TCF-activated sludges were detected. The kinetics of the development of these differences in treatment plants and their relationships with treatment efficiency and production process conditions should now be evaluated.
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".