Terpene yield of native spearmint ( <i>Mentha spicata</i> ) increases proportionally to photosynthetic photon flux density
Bibliographic record
Abstract
Native spearmint ( Mentha spicata L.) is a high-value medicinal crop cultivated for its terpene-rich essential oil, yet limited research exists on its response to photosynthetic photon flux density (PPFD) in controlled environments (CEs). This study evaluated the effects of canopy-level average photosynthetic photon flux density (APPFD) on the biomass, essential oil production, and morphology of M. spicata “Spanish” grown in a nutrient film technique system under sole-source LED lighting. An intensity gradient was employed where plants were exposed to APPFD levels ranging from 176 to 827 µmol·m −2 ·s −1 . Shoot dry mass, leaf dry mass, and total terpene yield increased linearly with the increase of APPFD, while essential oil concentration increased asymptotically. The linear increase in terpene yield was strongly correlated with an increase in leaf biomass rather than terpene concentration. No effects of APPFD on plant height or width were observed, but there was a statistically significant decrease in mainstem and internode length as APPFD increased. Leaves were smaller and thicker under higher APPFD, but total leaf area per plant increased. This study provided novel insight into the growth and terpene production of M. spicata “Spanish” under sole-source LED lighting in CEs and informs the development of lighting strategies aimed at enhancing essential oil 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".