Dual Utilization of Medicinal and Aromatic Crops as Bioenergy Feedstocks
Bibliographic record
Abstract
Dual production of biofuels and chemicals can increase the economic value of lignocellulosic bioenergy feedstocks. We compared the bioenergy potential of several essential oil (EO) crops with switchgrass ( Panicum virgatum L.), a crop chosen to benchmark biomass and lignocellulosic biofuel production. The EO crops of interest were peppermint ( Mentha × piperita L.), “Scotch” spearmint ( Mentha × gracilis Sole), Japanese cornmint ( Mentha canadensis L.), and sweet sagewort ( Artemisia annua L.). We also assessed each crop for EO production in a marginal production environment in Wyoming, USA, with irrigation and nitrogen (N) rates using a split-plot experimental design. Oil content ranged from 0.31 to 0.4% for Japanese cornmint, 0.23 to 0.26% for peppermint, 0.38 to 0.5% for spearmint, and the overall mean of sweet sagewort was 0.34%. Oil yields ranged from (in kg ha –1 ) 34 to 165 in Japanese cornmint, 25 to 108 in peppermint, 29.3 to 126 in spearmint, and 39.7 in sweet sagewort. EO production, but not composition, was sensitive to N fertilization. The alternative bioenergy crops and switchgrass produced similar amounts of ethanol from bench-scale simultaneous saccharification and fermentation assays. Value-added incomes from the EO proceeds were estimated to be between $1055 and $5132 ha –1 from peppermint, $1309 and $5580 ha –1 from spearmint, $510 and $2460 ha –1 from Japanese cornmint, and $3613 ha –1 from sweet sagewort under Wyoming growth conditions. The advantage of the proposed crops over traditional lignocellulosic species is the production of high-value natural products in addition to lignocellulosic biofuel production.
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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".