The Use of PGPB to Promote Plant Hydroponic Growth
Bibliographic record
Abstract
Improvements to the world's food supply chain are needed to ensure sufficient food is produced to meet increasing population demands. Growing food in soilless hydroponic systems constitutes a promising strategy, as this method utilizes significantly less water than conventional agriculture, can be situated in urban areas, and can be stacked vertically to increase yields per acre. However, further research is needed to optimize crop yields in these systems. One method to increase hydroponic plant yields involves adding plant growth-promoting bacteria (PGPB) into these systems. PGPB are organisms that can significantly increase crop yields via a wide range of mechanisms, including stress reduction, increases in nutrient uptake, plant hormone modulation, and biocontrol. The aim of this review is to provide critical information for researchers on the current state of the use of PGPB in hydroponics so that meaningful advances can be made. An overview of the history and types of hydroponic systems is provided, followed by an overview of known PGPB mechanisms. Finally, examples of PGPB research that has been conducted in hydroponic systems are described. Amalgamating the current state of knowledge should ensure that future experiments can be designed to effectively transition results from the lab to the farm/producer, and the consumer.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".