Characterization of Slow Pyrolysis Wood Vinegar and Tar from Banana Wastes Biomass as Potential Organic Pesticides
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Slow pyrolysis process has been used in the recent past to yield wood vinegar from various biomass wastes with a quest to investigate their chemical composition and possible uses. This study utilizes the abundant banana wastes in Uganda including leaves, pseudostem and peels (mpologoma, kisansa and kibuzi species) in the slow pyrolysis process to yield vinegar, tar and biochar. Characterization of these banana wastes’ vinegar and tar fractions were investigated via chromatographic and physicochemical analysis. The principle compounds present in the banana wastes vinegar and tar as per percentage peak areas were acids (68.6%), alcohols (62.5%), ketones (27.6%), phenols (25.7%) and furans (21.8%). The products characterization indicate that vinegar and tar contain compounds that can be used as pesticides, termiticide, fungicides, insect repellants, anti-leaching and soil degradation agents. Thus wood vinegar and tar can have sustainable impacts on agricultural sectors and chemical industries especially for developing countries.
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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 it