Advancement in Indoor Vertical Farming for Microgreen Production
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
With the growing global urban population and the emergence of megacities, there is a huge demand for arable land to meet the food demand and reduce malnutrition. Conventional agricultural practices lead to deforestation of the land for crop production and agricultural intensification to produce higher yield per unit area. These activities have been established to have negative impact on the environment thereby causing soil and water pollution. It is important to consider the use of vertical farming technology, which utilizes both horizontal and vertical space, and efficiently uses nutrients, water, and time (off season production with artificial lighting) more effectively to produce higher yield per unit volume of space than the conventional outdoor farming. Microgreens are taken into consideration to be grown under innovative vertical farming technology since they are rich in phytonutrients and they can be harvested in a short period of time. This paper reviews the current growing conditions of microgreens in vertical farming such as crop selection, media, light, nutrient solution, and containers while identifying knowledge gaps. Further, study in this area may lead to improved growing conditions to help solve the global issues and challenges surrounding food security, safety, and resource optimization.
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.
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 | 0.001 | 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 it