Insecticidal Activity of Bio-oil from the Pyrolysis of Straw from <i>Brassica</i> spp.
Bibliographic record
Abstract
Agricultural crop residues can be converted through thermochemical pyrolysis to bio-oil, a sustainable source of biofuel and biochemicals. The pyrolysis bio-oil is known to contain many chemicals, some of which have insecticidal activity and can be a potential source of value-added pest control products. Brassicacae crops, cabbage, broccoli, and mustards, contain glucosinolates and isocyanates, compounds with recognized anti-herbivore activity. In Canada, canola Brassica napus straw is available from over 6 000 000 ha and mustard Brassica carinata and Brassica juncea straw is available from 200 000 ha. The straw can be converted by microbial lignocellulosic enzymes as a substrate for bioethanol production but can also be converted to bio-oil by thermochemical means. Straw from all three species was pyrolyzed, and the insecticidal components in the bio-oil were isolated by bioassay-guided solvent fractionation. Of particular interest were the mustard straw bio-oil aqueous fractions with insecticidal and feeding repellent activity to Colorado potato beetle larvae. Aqueous fractions further analyzed for active compounds were found not to contain many of the undesirable phenol compounds, which were previously found in other bio-oils seen in the dichloromethane (DCM) and ethyl acetate (EA) solvent phases of the present study. Identified within the most polar fractions were hexadecanoic and octadecanoic fatty acids, indicating that separation of these compounds during bio-oil production may provide a source of effective insecticidal compounds.
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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".