Acid and alkaline treatments for enhancing the growth of rhizobia in sludge
Bibliographic record
Abstract
Wastewater sludges have been proposed as an effective media for the production of rhizobia. The effect of total suspended solid (TSS) concentrations and pretreatments of sludge on the growth of Sinorhizobium meliloti were investigated. Acid (pH 2.0-6.0 obtained with H2SO4) and alkaline (50-200 mequiv.wt./L of NaOH) treatments were applied to enhance the biodegradability of primary (0.325%-3.2% TSS obtained by dilution of original sample) and secondary (0.2%-0.4% TSS obtained by concentration of original sample) sludges. In primary sludge without pretreatment, the highest cell count (11.10 x 10(9) cfu/mL) was obtained with 1.3% TSS. However, a maximum cell count of 13.00 x 10(9) cfu/mL was reached using an acid treatment of pH 2.0 and a 0.325% TSS concentration. Moreover, the alkaline treatment with 100 mequiv.wt./L of NaOH and 0.65% TSS increased the cell yield to 21.00 x 10(9) cfu/mL. For secondary sludge without pretreatment, no enhancement of growth was observed while increasing TSS concentration. This may be due to the increase of inhibitory substances, such as heavy metals, and of the Ca and Mg concentrations. As in primary sludge, some acid and alkaline treatments of secondary sludge tend to improve the cell count of S. meliloti. However, the highest value of 9.80 x 10(9) cfu/mL obtained with 0.4% TSS at pH 2.0 was lower than that obtained with primary sludge. It was also observed that S. meliloti grown in treated sludges maintained its capacity to nodulate alfalfa.
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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 | 0.000 | 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".