Screening of Microorganisms with High Biological Activity to Create Consortia as A Growth Stimulator for Wheat Seeds
Bibliographic record
Abstract
Addressing the pressing need for more sustainable farming practices that concurrently enhance crop productivity, this study focuses on the identification of beneficial microorganisms and their impact on wheat seed germination. Through rigorous screening of microorganisms hailing from the wheat rhizosphere, a targeted approach was adopted to formulate microbial consortia, aiming for an additive effect in boosting plant growth. In the initial stage, a comprehensive screening was conducted on microorganisms isolated from the wheat rhizosphere soil. Subsequently, the influence of the culture liquids from these isolates, along with those of selected microorganism strains from established collections, on the growth rates of wheat was meticulously examined. These methodical investigations were instrumental in the formation of the microbial consortia. From an extensive pool of 35 collection strains and 16 isolates, microorganisms demonstrating the most significant positive impact on wheat growth were selectively chosen. Three potent consortia were subsequently formulated from these beneficial microorganisms. Although these findings are yet to be validated through practical application, the results offer promising prospects for their utilization in the agricultural sector. The identified microbial consortia present a green alternative to conventional fertilisers, thereby potentially contributing to the advancement of sustainable agriculture practices.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".