Development of sludge based stable aqueous Bacillus thuringiensis formulations
Bibliographic record
Abstract
This study focuses on development of aqueous flowable (suspension) formulations for Bacillus thuringiensis (Bt) based biopesticides from wastewater sludge. Different inerts like sorbitol, sodium monophosphate, sodium metabisulphite, sorbic acid, propionic acid, Tween-80, Triton X-100 and glycerol were tested for formulations. Five different formulations for non-hydrolyzed (NH) secondary sludges were tried and the best combination selected on the basis of various physical parameters like viscosity, particle size, suspendibility, entomotoxicity, and microbiological purity tests. F5 formulations (for secondary sludge) comprising sorbitol, sodium monophosphate and sodium metabisulphite gave better physical and biological characteristics with a small effect on entomotoxicity and spore concentration after 120 days at pH 6, 6.5 and temperatures 40 and 50 degrees C and viscosity change at 40 and 50 degrees C. The formulations were more stable at pH 4.0 to 5.0 and temperatures 4 to 30 degrees C whereas at pH 6.0 and 6.5 and temperatures 40 and 50 degrees C, there was degeneration of the product. Lower proteolytic activity and physical factors like ionic strength and surface group changes at pH 6 and 6.5 were responsible for the instability of the formulation.
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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 | 0.001 |
| 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".