Microbial Derived Compounds, a Step Toward Enhancing Microbial Inoculants Technology for Sustainable Agriculture
Bibliographic record
Abstract
Sustainable agriculture remains a focus for many researchers, in an effort to minimize environmental degradation and climate change. The use of plant growth promoting microorganisms (PGPM) is a hopeful approach for enhancing plant growth and yield. However, the technology faces a number of challenges, especially inconsistencies in the field. The discovery, that microbial derived compounds can independently enhance plant growth, could be a step toward minimizing shortfalls related to PGPM technology. This has led many researchers to engage in research activities involving such compounds. So far, the findings are promising as compounds have been reported to enhance plant growth under stressed and non-stressed conditions in a wide range of plant species. This review compiles current knowledge on microbial derived compounds, taking a reader through a summarized protocol of their isolation and identification, their relevance in present agricultural trends, current use and limitations, with a view to giving the reader a picture of where the technology has come from, and an insight into where it could head, with some suggestions regarding the probable best ways forward.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".