Effect of distillation waste water and plant hormones on spearmint growth and composition
Bibliographic record
Abstract
BACKGROUND: Distillation waste water (DWW) is a by-product from steam distillation of essential-oil crops; and currently, it is discharged into streams and rivers. The effects of DWW from 13 essential-oil crops, extracts from two alkaloid-containing species, and three plant hormones (methyl jasmonate, MJ; gibberellic acid, GA3; and salicylic acid, SA) were evaluated on productivity, essential-oil content and composition of spearmint (Mentha spicata L.) cv. 'Native'. RESULTS: Spearmint plant height was increased by the application of GA3 and Melissa officinalis DWW but suppressed by the application of Rosmarinus officinalis and Tagetes lucida DWW. Generally, MJ, GA3 and M. officinalis and Mentha arvensis DWW increased dry yields. The concentration of L-carvone in the oil ranged from 550 g kg(-1) (with Monarda citriodora DWW) to 670 g kg(-1) (with T. lucida DWW). M. citriodora DWW reduced the concentration of L-carvone in the oil by 23% relative to the control. CONCLUSION: Results suggest that DWW from essential-oil crops may affect monoterpene synthesis in M. spicata and, hence, may have a direct effect on the essential oil composition. DWW from essential-oil crops may be used as a growth promoter and modifier of the essential oil composition of spearmint.
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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) | 0.001 | 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 | 0.001 |
| 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".