Development of efficient suspension formulation of starch industry wastewater grown Sinorhizobium Meliloti for agricultural use.
Bibliographic record
Abstract
Liquid and alfalfa seed coated formulations of \nSinorhizobium meliloti were developed as potential bio \ninoculant. S. meliloti used in the present study was \nsuccessfully cultivated using starch industry wastewater as \nthe feedstock. The additives, such as sucrose, sorbitol, \npolyvinylpyrrolidone (PVP), polyethylene glycol (PEG), \nsodium alginates, and different mixtures of these compounds \nwere evaluated for their capacity to maintain the viability of \nS. meliloti during 13 weeks of refrigerated storage. All \nformulations were found to be capable of maintaining \nviable cell count greater than 10⁹ CFU/mL Sucrose (10% \nw/v) was found to be the best additive to extend the half \nof the formulation to as long as 83 weeks. During 4 months of \nseed storage at ambient temperature, fresh liquid \nformulation of PEG and PEG-sorbitol applied to alfalfa seeds \nmaintained the cell viability of 103 CFU/seed, which is \nequivalent to Canadian standards for commercial \nformulations. Micrographs showed that S. meliloti cells were \ndistributed differently on the seed surface (as aggr \nuniformly distributed) according to the additives used. 13 \nweek old PVP-sorbitol formulation applied on alfalfa seed \nwas the most efficient formulation to facilitated highest shoot \nyield on dry biomass basis.
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.
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".