The major Nod factor of Bradyrhizobium japonicum promotes early growth of soybean and corn
Bibliographic record
Abstract
Greenhouse experiments were conducted to evaluate the effect of Nod factor Nod Bj-V (C18:1, MeFuc) of Badyrhizobium japonicum on the growth of soybean and corn. Three-day-old seedlings of soybean and corn were grown in hydroponic solutions containing four concentrations (0, 10(-7), 10(-9) or 10(-11) M) of Nod factor. After 7 d of treatment, Nod factor enhanced soybean and corn biomass. Nod factor elicited profound effects on root growth resulting in 34-44% longer roots in soybean. More detailed analyses of the roots, using a scanner based image analysis system, revealed that Nod factor increased the total length, projected area and surface area of the roots and decreased the diameter of soybean roots, while it increased the total length of corn roots. Stem injection of soybean plants with 10(-7) M Nod factor resulted in increased dry matter accumulation. These results suggest that Nod factor, besides mediating early stages of nodulation, has more general plant growth-promoting effects.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
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 itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
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".