Screening of Microorganisms with High Biological Activity to Create Consortia as A Growth Stimulator for Wheat Seeds
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it