Invasive Plant Biomass as a Source of Lipids for Bioeconomy
Bibliographic record
Abstract
Abstract Invasive plants can be considered as a significant environmental problem: a direct threat to biodiversity but also affecting the productivity of agriculture forestry as well as human and animal health. Considering the threats by invasive plants European as well as other countries put efforts into invasive plant spreading control and eradication of existing populations. Invasive plant biomass at the same time can be a valuable resource for bioeconomy. The study aims to evaluate the possibilities of using invasive plant biomass as a source of biologically and pharmacologically active substances – lipids and fatty acids. Invasive plants common in North Europe have been studied: lupine, Canadian goldenrod and Japanese, Bohemian and Sakhalin knotweeds. For extraction traditionally used solvents were compared with green (low toxicity, biogenic origin) solvents and the good performance of the environmentally friendly solvents has been demonstrated. Bohemian knotweed exhibits higher proportions of certain fatty acids such as linoleic acid and eicosanic acid in comparison to other species. Japanese knotweed, on the other hand, generally displays intermediate levels for most fatty acids but stands out with distinct peaks in components such as linolenic acid. In contrast, Sakhalin knotweed dominates in several fatty acids including palmitic acid which highlights its unique biochemical profile. Thus, invasive plants can serve as valuable resources of biologically active compounds for differing applications and their biomass biorefinery can serve as a resource thus supporting invasive plant eradication efforts.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".