The response of soybean to nod factors and a bacteriocin
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
Microbe-to-plant signals can enhance the growth of a wide range of crops. The responses by soybean (Glycine max var. 91M01) to 2 signal molecules were investigated: Bradyrhizobium japonicum 532C lipo-chitooligosaccharide (Nod Bj V [C:18, MeFuc]) (LCO); and Bacillus thuringiensis strain NEB17 bacteriocin thuricin 17 (Th17). The objective was to assess and quantify the response by soybean, in terms of factors that contribute to yield, to the experimental signal molecules in germination experiments and field experiments. Soybean germination was stimulated by the experimental concentrations of Th17 under controlled 15°C and 22°C conditions, and 10−6 M LCO under 15°C. There were negative relationships between Th17 concentration and both the number of trifoliate leaves and the dry weight of nodules: lower concentrations resulted in plants with more leaves and nodules while higher concentrations resulted in plants with fewer leaves and nodules. The 10−8 M LCO treatment had a significant effect on the dry weight of nodules at the flowering stage of plant development (F4,21 = 6.06, p = 0.0019). Considering the harvest stage data from both field trials of 2011, the lower experimental concentrations of Th17 resulted in taller plants. The study of Th17 has the potential to expand our understanding of this relatively recent and unexpected finding; and to understand how best to apply this finding, to allow increased production of soybean. Collectively, these results indicate that Th17 has potential in this regard.
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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 | 0.000 | 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