Development of emulsion from rhizobial fermented starch industry wastewater for application as <i>Medicago sativa</i> seed coat
Bibliographic record
Abstract
Abstract Starch industry wastewater was efficiently employed for the production of Sinorhizobium meliloti and the concentrated culture was used for the development of a biofertilizer formulation. Tween‐80 (0.02 g/L) acted as the best emulsifier for a Sinorhizobium–canola oil emulsion. The stability of the emulsion and survival of the organism was enhanced by supplementation of xanthan gum at pH 8. The refrigerated condition was most favorable for stability and survival of the microorganism. The survival of microorganism at 4±1°C was 2.78×1010 and 2.01×1010 CFU (colony forming unit)/mL on storage for 1 and 2 months, respectively. The values were higher than the prescribed cell count (×103 CFU/mL) for field application. At 40°C, the survival of bacteria reduced from 3×1010 CFU/mL to 8.1×109 and 8.8×106 CFU/mL in 1 and 2 months, respectively. Emulsion‐coated seed was incubated at different temperatures and a cell count of 105 CFU/seed was observed after 2 months of storage at 4°C, which was equal to the highest level of the described requirement (103–105 CFU/seed). Emulsion supplemented with xanthan gum improved the shelf‐life under optimized conditions (Sinorhizobium concentrate – canola oil (1:1) emulsion with 0.02 g/L Tween‐80; storage at pH 8 and temperature 4±1°C) and this emulsion with the required cell count and prolonged viability was used for the pre‐inoculation of seed or for in situ soil application.
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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) | 0.000 | 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 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".