Towards a tailored indoor horticulture: a functional genomics guided phenotypic approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As indoor horticulture gathers momentum, electric (also termed artificial) lighting systems with the ability to generate specific and tunable wavelengths have been developed and applied. While the effects of light quality on plant growth and development have been studied, authoritative and reliable sets of light formulae tailored for the cultivation of economically important plants and plant traits are lacking as light qualities employed across laboratories are inconsistent. This is due, at least in part, to the lack of molecular data for plants examined under electric lights in indoor environments. It has hampered progress in the field of indoor horticulture, in particular, the transition from small-scale indoor farming to commercial plant factories. Here, we review the effects of light quality on model and crop plants studied from a physiological, physical and biochemical perspective, and explain how functional genomics can be employed in tandem to generate a wealth of molecular data specific for plants cultivated under indoor lighting. We also review the current state of lighting technologies in indoor horticulture specifically discussing how recent narrow-bandwidth lighting technologies can be tailored to cultivate economically valuable plant species and traits. Knowledge gained from a complementary phenotypic and functional genomics approach can be harvested not only for economical gains but also for sustainable food production. We believe that this review serves as a platform that guides future light-related plant research.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 it