Spectral effects of blue and red light on growth, anatomy, and physiology of lettuce
Bibliographic record
Abstract
Characterizing spectral effects of blue and red light ratios on plants could help expand our understanding of factors that regulate growth and development, which is becoming increasingly important as narrowband light-emitting diodes become common for sole-source lighting. Herein we report growth, physiological, and anatomical responses of two lettuce cultivars grown indoors under various blue and red ratios including monochromatic treatments. When used in combination with red, increasing the proportion of blue light generally reduced growth but increased chloroplast abundance and single-leaf photosynthetic efficiency. However, when used as single wavebands, both blue and red light increased leaf area and epidermal cell area, but reduced root dry mass, SPAD index, stomatal density, and leaf thickness compared to dichromatic light. In addition, chloroplast abundance and single-leaf physiological responses were higher in plants grown under monochromatic blue compared to red light, but the opposite trend was measured for shoot biomass. Our results show that spectral effects on morpho-anatomical leaf responses can largely influence plant growth and single-leaf physiological responses. However, a significant blue light reduction in radiation capture ultimately limits growth and productivity of lettuce plants when dichromatic blue and red light is used.
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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 | 0.001 |
| 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".