Regrowth of bacterial pathogen indicators after advanced electro-osmotic dewatering of biosolids
Bibliographic record
Abstract
Applying an electrical field for 10 min to mechanically dewatered biosolids increased dryness to 35 to 45% and reduced Escherichia coli levels to/close to detection limits. Possible reasons for the regrowth of bacterial pathogens impacted by electro-osmotic dewatering are biosolids dryness, low pH and the generation of bacterial oxidants on the anode due to high temperatures. Testing for this with aerobic and anaerobic incubations resulted in more than four logs of E. coli regrowth in 2011, when biosolids harboured higher initial counts; whereas regrowth was not observed in 2012, when biosolids had lower E. coli counts. In 2011 experiments, however, addition of a nitrate salt prior to anaerobic incubations reduced regrowth by 1.4 logs. This indicates that other NO3−-respiring organisms could be competing with E. coli and diverting electrons away from the E. coli population. These results suggest that electro-osmotic dewatering and nitrate can play a role in controlling E. coli regrowth.
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 | 0.000 |
| 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) | 0.001 | 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".