Effect of Plant Hormones and Distillation Water on Mints
Bibliographic record
Abstract
Steam distillation of essential oil crops produces residual distillation wastewater that is released into the environment. This study evaluated the effects of three plant hormones [methyl jasmonate (MJ); gibberellic acid (GA3); and salicylic acid (SA)] at three concentrations and the residual distillation water from 15 plant species applied as foliar spray on biomass yields, essential oil content, and essential oil yield of Mentha ×piperita ‘Black Mitcham’ and Mentha spicata ‘Native’. Overall, the application of SA at 1000 mg·L −1 increased biomass yields of both species. More treatments influenced essential oil content in ‘Black Mitcham’ peppermint than in ‘Native’ spearmint. Application of MJ at 100 and 1000 mg·L −1 , GA3 at 10 mg·L −1 , SA at 10 or 100 mg·L −1 , and distillation water of Achillea millefolium , Ammi majus , Artemisia absinthium , Cymbopogon flexuosus , Cymbopogon martinii , Chrysanthemum balsamita , and Hypericum perforatum increased the essential oil content of peppermint, whereas the oil content of spearmint was increased only by application of Monarda fistulosa distillation water. Application of MJ at 100 mg·L −1 , SA at 100 mg·L −1 , and A. absinthium , C. flexuosus , and C. balsamita distillation waters increased essential oil yields of peppermint, whereas the application of SA at 1000 mg·L −1 and distillation water of A. absinthium , Lavandula vera , and M. fistulosa increased oil yields of spearmint. This study demonstrated that the residual distillation water of some aromatic plant species may be used as a tool for increasing essential oil content or essential oil yields of peppermint and spearmint crops.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".