Short-Term Evaluation of Woodland Strawberry in Response to Melatonin Treatment under Low Light Environment
Bibliographic record
Abstract
The cultivation of strawberries in controlled environments presents challenges related to environmental stressors, especially insufficient light. Melatonin, as a widely investigated plant growth regulator, was considered as a potential candidate to mitigate damage, and enhance photosynthesis stability. However, whether melatonin can improve photosynthesis under light deficiency in woodland strawberry (Fragaria vesca) remains elusive. In this study, we evaluated gas exchange parameters, Chlorophyll fluorescence parameters, photochemical efficiency, and the related genes’ expression levels to decipher the multifaceted impact of melatonin on photosynthesis. We found concentration-dependent effects of melatonin on photosynthetic parameters, with potential benefits at lower concentration and inhibitory effects at higher concentration. Notably, melatonin increased non-photochemical quenching (NPQ), a mechanism for dissipating excess light energy, while leaving photochemical quenching (qP) relatively stable. Further analysis showed that melatonin up-regulated key xanthophyll cycle-related genes (DHAR, VDE, and PsbS), indicating its involvement in energy dissipation processes. In conclusion, our study uncovered the dual and complex role of melatonin in the short-term response of photosynthesis in woodland strawberries under low-light conditions.
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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) | 0.001 | 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".