The Effect of Trap Plants on the Population Diversity of Bradyrhizobium japonicum
Bibliographic record
Abstract
One hundred and four isolates of Bradyrhizobium japonicum were isolated from nodules of two different trap plants, Viz. Soya bean cultivars, Maple Glen and Orford which were inoculated with two different soilsamples (Ottawa and St-Hugus soils). All isolates were clustered based on PCR/RFLP of 16S-23S rRNAgenes. RFLP analysis was performed to characterize all the isolates using six different endonucleaseenzymes. The data was analyzed by using Jamp software. Using dendrogram data, all the isolates weregrouped into six different clusters. There were four and five clusters of Bradyrhizobium japonicum in Ottawaand St-Hugus soils, respectively. Three clusters were common between two cultivars of Soya bean wheninoculated with Ottawa soil and four common clusters were recognized when trap plants inoculated with St-Hugus soil. In Ottawa soil, cluster I was not detected by Orford cultivar, likewise in St-Hugus soil, cluster VIwas not detected by Maple Glen cultivar of Soya bean. Isolates of cluster III were dominantly trapped whenMaple Glen and Orford cultivars inoculated with Ottawa soil but isolates from clusters I, IV and III weretrapped when they were inoculated with St-Hugus soil. Since different cultivars trapped different isolate typesit can be concluded that for population studies of rhizobial bacteria different trap plants can provide a bettercomposition of native population of bacteria.
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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 | 0.000 | 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 | 0.001 |
| 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 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".