Study of the beneficial effects of green light on lettuce grown under short‐term continuous red and blue light‐emitting diodes
Bibliographic record
Abstract
Red and blue light are the most important light spectra for driving photosynthesis to produce adequate crop yield. It is also believed that green light may contribute to adaptations to growth. However, the effects of green light, which can trigger specific and necessary responses of plant growth, have been underestimated in the past. In this study, lettuce (Lactuca sativaL.) was exposed to different continuous light (CL) conditions for 48 h by a combination of red and blue light‐emitting diodes (LEDs) supplemented with or without green LEDs, in an environmental‐controlled growth chamber. Green light supplementation enhanced photosynthetic capacity by increasing net photosynthetic rates, maximal photochemical efficiency, electron transport for carbon fixation (JPSII) and chlorophyll content in plants under the CL treatment. Green light decreased malondialdehyde and H2O2accumulation by increasing the activities of superoxide dismutase (EC 1.15.1.1) and ascorbate peroxidase (EC 1.11.1.11) after 24 h of CL. Supplemental green light significantly increased the expression of photosynthetic genesLHCbandPsbA from 6 to 12 h, and these gene expressions were maintained at higher levels than those under other light conditions between 12 and 24 h. However, a notable downregulation of bothLHCbandPsbA was observed during 24 to 48 h. These results indicate that the effects of green light on lettuce plant growth, via enhancing activity of particular components of antioxidative enzyme system and promoting ofLHCbandPsbA expression to maintain higher photosynthetic capacity, alleviated a number of the negative effects caused by CL.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 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".