Production of<i>S. Meliloti</i>Using Wastewater Sludge as a Raw Material: Effect of Nutrient Addition and pH Control
Bibliographic record
Abstract
The utilization of wastewater sludge as a raw material for the production of legume inoculant is considered as a new viable alternative for recycling. The effect of addition of nutrient sources (yeast extract and glycerol) and the pH control on S. meliloti was investigated in a shake flask and at controlled pH in a 15 l fermentor. Different concentrations of yeast extract (0.5, 1, 2 and 4 g l(-1)) and glycerol (2.5, 5, 7.5 and 10 g l(-1)) were added to the secondary sludge. The cell yield as well as the generation time were affected by the addition of these nutrient sources. The maximum cell yield (8.85x10(9) cfu ml(-1)) was achieved in 32 hours of incubation with the addition of 4 g l(-1) of yeast extract. This value was 3.2 times higher than from the non-supplemented sludge. Moreover, at this yeast extract concentration, the cell concentration in the stationary phase did not decrease. The addition of glycerol to sludge samples containing 4 g l(-1) of yeast extract further improved the rhizobial growth but not significantly compared with the control. The highest yield (16.5x10(9) cfu ml(-1)) was obtained with 7.5 g l(-1) of glycerol and 4 g l(-1) of yeast extract. In fermentor experiments, pH did not seem to be a limiting factor and the increase of pH up to 8.85 in uncontrolled fermentor seems to have no effect on rhizobial growth and cell yield.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".