Screening of Different Adjuvants for Wastewater/Wastewater Sludge-Based <I>Bacillus thuringiensis</I> Formulations
Bibliographic record
Abstract
Screening of different adjuvants, namely, suspending agents, phagostimulants, stickers, antimicrobial agents, and UV screens to develop aqueous biopesticidal suspensions of Bacillus thuringiensis (Bt) variety kurstaki HD-1 fermented broths, specifically, nonhydrolyzed sludge, hydrolyzed sludge, starch industry wastewater, and soya (commercial medium), were investigated. The selected suspending agents [20% (wt:vol)] included sorbitol, sodium monophosphate, and sodium metabisulfite with corresponding suspendibility of 74-92, 69-85, and 71-82%, respectively. Molasses [0.2% (wt:vol)] increased adherence by 84-90% for all fermented broths. The optimal phagostimulants [0.5% (wt:vol)], namely, soya and molasses, caused entomotoxicity increase of 3-13 and 7-13%, respectively. Sorbic and propionic acids showed high antimicrobial action [0.5% (wt:vol)], irrespective of fermentation medium. Sodium lignosulfonate, molasses, and Congo red, when used as UV screens [0.2% (wt:vol)], showed percent corresponding entomotoxicity losses of 3-5, 0.5-5 and 2-16, respectively. The Bt formulations, when exposed to UV radiation, showed higher half-lives (with and without UV screens) than the fermented broths or semisynthetic soya medium and commercial Bt formulation. UV screen-amended nonhydrolyzed, hydrolyzed, and starch industry wastewater formulations showed 1.3-1.5-fold higher half-lives than commercial Bt formulation. Thus, the recommended formulation comprises sorbitol, sodium monophosphate, sodium metabisulfite (suspending agents); molasses, soya flour (phagostimulants); molasses and skimmed milk powder (rainfasteners); sorbic and propionic acids (antimicrobial agents) and sodium lignosulfate; and molasses and Congo red (UV screens). These waste-based Bt formulations offer better UV resistance in comparison with commercial formulation.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 teacher head, 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".