Enhancing plant productivity while suppressing biofilm growth in a windowfarm system using beneficial bacteria and ultraviolet irradiation
Bibliographic record
Abstract
Common problems in a windowfarm system (a vertical and indoor hydroponic system) are phytopathogen infections in plants and excessive buildup of biofilms. The objectives of this study were (i) to promote plant health by making plants more resistant to infection by using beneficial biosurfactant-producing Pseudomonas chlororaphis around the roots and (ii) to minimize biofilm buildup by ultraviolet (UV) irradiation of the water reservoir, thereby extending the lifespan of the whole system with minimal maintenance. Pseudomonas chlororaphis-treated lettuce grew significantly better than nontreated lettuce, as indicated by enhancement of color, mass, length, and number of leaves per head (p < 0.05). The death rate of the lettuce was reduced by ∼ 50% when the lettuce was treated with P. chlororaphis. UV irradiation reduced the bacteria (4 log reduction) and algae (4 log reduction) in the water reservoirs and water tubing systems. Introduction of P. chlororaphis into the system promoted plant growth and reduced damage caused by the plant pathogen Pythium ultimum. UV irradiation of the water reservoir reduced algal and biofilm growth and extended the lifespan of the system.
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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".