From Lab to Greenhouse: Shedding Light on the Role of Spectral Quality and CO2 Concentration in Tomato (Solanum lycopersicum L.) Production
Bibliographic record
Abstract
During the light limiting winter months in Canada and other northern countries, supplementary lighting is needed during greenhouse production to meet consumer demand for fresh vegetables. The advancements in light-emitting diodes (LEDs) have made them a viable source for supplementary lighting, with many advantages over traditionally high pressure sodium (HPS) luminaries. Due to their low heat emittance, compact design, and ability to supply wavelength specific light, LEDs provide an unparalleled flexibility in the development and implementation of lighting strategies for crop production. This thesis is an investigation of spectral quality and CO2 concentration and how these factors can affect tomato (Solanum lycopersicum L.) growth specifically tailored towards the implementation of LED lighting fixtures in commercial production. Fundamental pathways such as CO2 and H2O gas exchange at both the leaf and whole plant level as well as the process of carbon export were examined under a variety of spectral qualities and CO2 concentrations. Results from experiments involving whole plant gas exchange and carbon export were then used during a production style greenhouse experiment where the effects of continuous lighting on tomato physiology and yield were assessed. Results from this thesis aim to not only increase our understanding of how fundamental pathways respond to abiotic stress, but also how utilizing laboratory data can aid in the design and implementation of greenhouse lighting strategies to increase yield and sustainability of tomato production during light limiting periods.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".