Culturing the uncultured microbial majority in activated sludge: A critical review
Bibliographic record
Abstract
Activated sludge is a widely applied wastewater treatment process that mainly uses suspended microbial flocs to remove pollutants in wastewater. With the characteristics of high biomass content and high microbial diversity, activated sludge plays an important role in pollutant removal and contains various functional microorganisms as a valuable pool of various useful microbial resources. However, the majority of microorganisms in activated sludge have not been isolated, which substantially limits the improvement of treatment efficiency and the innovation of process technology in wastewater engineering. As the basic biological methodology which can extremely expand the downstream studies for microorganisms, the cultivation of new species in activated sludge is urgently needed to fill the gaps between the cultured and uncultured microbial communities. The growing emphasis on cultivation in recent years has spawned the creation of many innovative and high-throughput cultivation techniques. In this review, we summarized the microorganism “wanted list” in activated sludge, reviewed the potential cultivation methods that could extend our understanding of activated sludge microbiota, and discussed the significance and perspectives for activated sludge microbiota cultivation.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".