Wastewater Sludge as a New Medium for Rhizobial Growth
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.004 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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 it