Biochar and gypsum amendment of agro-industrial waste for enhanced black soldier fly larval biomass and quality frass fertilizer
Bibliographic record
Abstract
Black soldier fly (BSF) (Hermetia illucens L.) is one of the most efficient bio-waste recyclers. Although, waste substrate amendments with biochar or gypsum during composting process are known to enhance nutrient retention, their impact on agro-industrial waste have not been documented. Hence, this study focuses on a comparative effect of agro-industrial waste amended with biochar and gypsum on BSF larval performance, waste degradation, and nitrogen (N) and potassium retention in frass fertilizer. Brewery spent grain was amended with biochar or gypsum at 0, 5, 10, 15 and 20% to determine the most effective rates of inclusion. Amending feedstock with 20% biochar significantly increased wet (89%) and dried (86%) larval yields than the control (unamended feedstock). However, amendment with 15% gypsum caused decrease in wet (34%) and dried (30%) larval yields but conserved the highest amount of N in frass. Furthermore, the inclusion of 20% biochar recorded the highest frass fertilizer yield and gave a 21% increase in N retention in frass fertilizer, while biomass conversion rate was increased by 195% compared to the control. Feedstock amendment with 5% biochar had the highest waste degradation efficiency. Potassium content in frass fertilizer was also significantly enhanced with biochar amendment. At maturity, frass compost with more than 10% inclusion rate of biochar had the highest cabbage seed germination indices (>100%). The findings of this study revealed that initial composting of biochar amended feedstocks using BSF larvae can significantly shorten compost maturity time to 5 weeks with enhanced nutrient recycling compared to the conventional composting methods.
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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".