Evaluation of yeast inoculum seeding on the remediation of water and sediment in an urban river
Bibliographic record
Abstract
Abstract Yeast, widely used in wastewater treatment, can make use of organic matter and provide nutrition for other living things. The effect of yeast inoculum addition on the remediation of water and sediment in an urban river was evaluated. In the field test, average removal efficiency of CODCr, NH3‐N, and TN was 20 %, 25 %, and 25 %, respectively, in the addition segment after 12 days. After one year, the concentrations of TN and TOC in sediment were not significantly different between the addition and upstream segments. However, species of carbon chains, alkanes with lower boiling points, fatty acids, and esters increased significantly in the sediment after yeast addition, indicating a long‐term effect on remediation by increasing biodegradability. Based on two years of long‐term monitoring, the total numbers of bacteria, actinomycetes, and fungi in the sediment from the yeast addition segment were greater than those of the upstream segment. The sequencing results showed that the diversity of bacteria in the addition segment was better than that in the upstream segment. In summary, yeast addition had a significant effect on remediation in an urban river by increasing microbial number and diversity and providing a more favourable microbial environment.
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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".