Natural incidence of endophytic bacteria in pea cultivars under field conditions
Bibliographic record
Abstract
Pea plants grown in the field were used to study the natural incidence of endophytic bacteria in the stem. Eleven pea cultivars at the flowering stage were screened for the presence of endophytic bacteria using a printing technique with surface disinfested stem cross-sections on 5% Trypticase Soy Agar (TSA). Five cultivars showed colonization. Cultivar Twiggy showed the highest and most consistent colonization and was further investigated. Stems of cv. Twiggy at the pod stage were analyzed for endophytic bacterial types and populations. Cross-sections of surface disinfested stems were printed on 5% TSA. Endophytic bacterial populations decreased from the lower to the upper part of the stem. One section from the third and the fourth internode was surface disinfested, homogenized, and spiral plated on the media 5% TSA, R2A, and SC (Davis et al. 1980). Over a series of 30 samples, 5% TSA gave significantly better recovery of bacterial endophytes compared with R2A and SC media. For most stems, populations ranged from 10(4) to 10(5) CFU/g except in one of the field blocks in which endophyte populations were uniformly higher. Comparison of colony counts by spiral plating and printing showed a positive correlation. The most frequently recovered bacterial types were Pantoea agglomerans and Pseudomonas fluorescens. Less frequently isolated were Pseudomonas viridiflava and Bacillus megaterium.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".