The Effect of Spectral Quality on Daily Patterns of Gas Exchange, Biomass Gain, and Water-Use-Efficiency in Tomatoes and Lisianthus: An Assessment of Whole Plant Measurements
Bibliographic record
Abstract
Advancements in light-emitting diode (LED) technology have made them a viable alternative to current lighting systems for both sole and supplemental lighting requirements. Understanding how wavelength specific LED lighting can affect plants is thus an area of great interest. Much research is available on the wavelength specific responses of leaves from multiple crops when exposed to long-term wavelength specific lighting. However, leaf measurements do not always extrapolate linearly to the complexities which are found within a whole plant canopy, namely mutual shading and leaves of different ages. Taken together, both tomato (Solanum lycopersicum) leaves under short-term illumination and lisianthus (Eustoma grandiflorum) and tomato whole plant (WP) diurnal patterns of plants acclimated to specific lighting indicate wavelength specific responses of both H2O and CO2 gas exchanges involved in the major growth parameters of a plant. Tomato leaves grown under a white light source indicated an increase in transpiration rate and internal CO2 concentration and a subsequent decrease in water-use-efficiency (WUE) when exposed to a blue LED light source compared to a green LED light source. Interestingly, the maximum photosynthetic rate was observed to be similar. Using plants grown under wavelength specific supplemental lighting in a greenhouse, a decrease in WP WUE was seen in both crops under both red-blue (RB) and red-white (RW) LEDs when compared to a high pressure sodium (HPS) light. Whole plant WUE was decreased by 31% under the RB LED treatment for both crops compared to the HPS treatment. Tomato WP WUE was decreased by 25% and lisianthus WP WUE was decreased by 15% when compared to the HPS treatment when grown under RW LED. The understanding of the effects of wavelength specific lighting on both leaf and WP gas exchange has significant implications on basic academic research as well as commercial greenhouse 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".