Introducing key microbes from high productive soil transforms native soil microbial community of low productive soil
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
This study aimed to understand the changes in rhizosphere microbial structure and diversity of an average corn yielding field site soil with the introduced microbial candidates from a high-yielding site. Soils used in this study were from two growers' fields located in Dunnville, Ontario, Canada, where one of the farms has an exceptional high corn yield (G-site soil; ca 20 tons/acre) and the other yields an average crop (H-site soil; 12 tons/acre) (8 years of unpublished A & L data). In growth room experiments using wheat as the indicator crop, calcium alginate beads with microbes composed of Azospirillum lipoferum, Rhizobium leguminosarum, Burkholderia ambifaria, Burkholderia graminis, Burkholderia vietnamiensis, Pseudomonas lurida, Exiguobacterium acetylicum, Kosakonia cowanii, and Paenibacillus polymyxa was introduced into the soil at planting to the average-yielding soil. These bacteria had been isolated from the high-yielding farm soil. Among the nine microbial candidates tested, three (P. polymyxa, E. acetylicum and K. cowanii) significantly impacted the plant health and biometrics in addition to microbial richness and diversity, where the microbial profile became very similar to the high productive G-site soil. One hundred and forty-two bacterial terminal restriction fragments (TRFs) were involved in the community shift and 48 of them showed significant correlation to several interacting soil factors. This study indicates the potential of shifting microbial profiles of average-yielding soils by introducing key candidates from highly productive soils to increase biological soil health.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it