Mechanisms of action of plant growth promoting bacteria
Bibliographic record
Abstract
The idea of eliminating the use of fertilizers which are sometimes environmentally unsafe is slowly becoming a reality because of the emergence of microorganisms that can serve the same purpose or even do better. Depletion of soil nutrients through leaching into the waterways and causing contamination are some of the negative effects of these chemical fertilizers that prompted the need for suitable alternatives. This brings us to the idea of using microbes that can be developed for use as biological fertilizers (biofertilizers). They are environmentally friendly as they are natural living organisms. They increase crop yield and production and, in addition, in developing countries, they are less expensive compared to chemical fertilizers. These biofertilizers are typically called plant growth-promoting bacteria (PGPB). In addition to PGPB, some fungi have also been demonstrated to promote plant growth. Apart from improving crop yields, some biofertilizers also control various plant pathogens. The objective of worldwide sustainable agriculture is much more likely to be achieved through the widespread use of biofertilizers rather than chemically synthesized fertilizers. However, to realize this objective it is essential that the many mechanisms employed by PGPB first be thoroughly understood thereby allowing workers to fully harness the potentials of these microbes. The present state of our knowledge regarding the fundamental mechanisms employed by PGPB is discussed herein.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".