Plant Growth Optimization Using Amber Light Supplemented with Different Blue Light Spectra
Bibliographic record
Abstract
Blue (400–500 nm) and red (600–700 nm) light regions have been investigated for their effects on photosynthesis and plant growth, yet evidence for specific blue light wavelengths in plant research is lacking. Investigations into amber (595 nm) light are similarly limited. To ‘shed light’ on these two important wavelengths, this study investigated the combined effects of blue and amber light on plant growth and development in two model plants: tomato (Solanum lycopersicum cv. Beefsteak) and lettuce (Lactuca sativa cv. Breen). Plant growth responses were determined with four light treatments: B+BA (blue + broad amber, 455–602 nm), RB-NA (royal blue + narrow amber, 430–602 nm), RB-BA (royal blue + broad amber, 423–595 nm), and high-pressure sodium at a PPFD of 250 µmol m−2 s−1. After 21 days, the highest fresh and dry mass for both plant species was obtained under the RB-BA light treatment. Shifting the blue wavelength from 430 nm to 455 nm with broad amber lighting led to 40% less fresh mass for tomatoes, whereas only an approximate 5% reduction in fresh mass was observed for lettuce plants. Our findings demonstrate that an alternate and combined blue + amber light spectrum is effective for optimizing plant productivity.
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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".