Intracanopy lighting strategies to improve green bush bean (Phaseolus vulgaris) compatibility with vertical farming
Bibliographic record
Abstract
Now that multi-tiered plant factories with artificial lighting (PFALs) have demonstrated sufficient proof of concept for leafy green and microgreen production; the next challenge is to determine the optimal environment conditions and horticultural management practices required to produce nutrient-dense plant-based protein (PBP) crops within these advanced controlled environment systems (CES). Sole-source lighting within PFALs is energetically and economically expensive, as such, optimizing light distribution through intracanopy lighting could be a key factor in expanding the number of crops compatible with PFAL production. An ideal PBP PFAL crop will have a compact morphology (height, area, volume), be compatible with low-light environments, be self-pollinating, and have a relatively short life cycle. The objectives of this study were to 1) evaluate a selection of green bush bean cultivars (Phaseolus spp.) within a CES to determine which currently available cultivar is most compatible with PFAL production and 2) determine if the addition of intracanopy LED lighting could further improve cultivar compatibility with PFAL systems. The bush bean cultivar “Bronco” was selected after a 40-day flowering and 60-day fruiting trial for its compact morphology and yield (count, fresh weight). Intracanopy LED lighting trials on “Bronco” demonstrated a reduced shoot height (16%), increased bean count (22%), and increased fresh bean weight (17%) relative to plants produced with overhead lighting alone. While intracanopy lighting improved green bush bean compatibility with PFAL production, the additional light applied within the canopy increased the cost of 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".