Wastewater Sludge as a New Medium for Rhizobial Growth
Bibliographic record
Abstract
Abstract The objective of this study was to demonstrate that municipal and industrial wastewater sludges could be used as a sole raw material to sustain growth of rhizobia. Growth of two different groups of rhizobium (fast growing: Sinorhizobium meliloti, Rhizobium leguminosarum bv viciae; and slow growing: Bradyrhizobium japonicum and Bradyrhizobium elkanii) was tested on primary, secondary and mixed sludges obtained from different wastewater treatment plants. The results obtained in Erlenmeyer flasks indicated that slow- and fast-growing rhizobia grew well in sludge. Generally, the number of cells of rhizobia exceeds 1 × 109 cfu/mL in 72 h. The composition of sludges varies with the sludge type and origin. The sludge composition affected the generation time, cell yield and nodulation index. Higher solids concentration tended to give higher generation time. The high sludge metals concentration did not affect the growth kinetics of rhizobia. However, primary sludge could inhibit cell growth. Acid, alkaline and oxidative pre-treatments increased the primary sludge biodegradability and consequently the cell count of S. meliloti. Pre-treatment of pulp and paper sludge with NaOH enhanced the bacterial cell concentration to a maximum 1 × 1010 cfu/mL. Sludge pre-treatment decreased the generation time and reduced the process time.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".