Bioenergy II: Characterization of the Pesticide Properties of Tobacco Bio-Oil
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
Pyrolysis converts biomass such as agricultural and forestry waste into bio-oil, preserving some chemicals while creating other, new ones. Nicotine, a chemical present in tobacco leaves and a known pesticide, was found to remain intact during pyrolysis. As expected, insecticidal properties were observed for tobacco bio-oil. Pesticide characteristics of tobacco bio-oil have been observed on the Colorado potato beetle (CPB), a pest currently resistant to all major insecticides, as well as a few bacteria and fungi that do not currently respond well to chemical treatment. Unexpectedly, nicotine-free fractions of the bio-oil were also found to be highly lethal to the beetles and successful at inhibiting the growth of select microorganisms. Through GC-MS, it was found that the active, nicotine-free fractions were rich in phenolics, chemicals likely created from lignin during pyrolysis. While bio-oils in general are known to contain phenolic chemicals, such as cresols, to our best knowledge, quantitative analysis has not been performed to determine if these chemicals are solely responsible for the observed pesticide activities. Based on GC-MS results, ten of the most abundant chemicals, eight of which were phenolic chemicals, were identified and examined through bio-assays. A mixture of these chemicals at the concentration levels found in the bio-oil did not account for the bio-oil activity towards the microorganisms. Tobacco bio-oil may have potential as a pesticide, however, further analyses using liquid chromatography is necessary to identify the remaining active chemicals.
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.
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