Method for simple and rapid enumeration of total epiphytic bacteria in the washing solution of rice plants
Bibliographic record
Abstract
The phyllosphere is one of the most common habitats for terrestrial bacteria. However, little is known about the populations of bacteria, including unculturable bacteria, that thrive on plant surfaces. Here, we developed a fluorescent nuclear staining technique to easily and rapidly observe and enumerate populations of total and living epiphytic bacteria, with particular emphasis on the concentration by centrifugation and fixation of the epiphytic bacteria. An investigation on the optimal conditions for centrifugation and fixation revealed that centrifugation at 20 400g for 2 min and fixation with 0.5% glutaraldehyde solution were the optimum conditions for observation of the bacteria. Using this technique, we assessed the populations of the total and living bacteria on the surface of rice plants. When epiphytic bacteria were recovered from rice seeds (Oryza sativa 'Koshihikari'), the number of total and living bacterial cells was 7.36 and 6.85 log₁₀·g⁻¹ (fresh mass) in the seed washing, respectively. In contrast, the numbers of total and living bacterial cells in the leaf sheath washings were 5.5-5.8 and 5.3-5.7 log₁₀·g⁻¹, respectively. Approximately 5%-30% of the total bacteria in the washing solution of rice plant were culturable. The usefulness of the enumeration method and the amount of bacteria on the plant surfaces are discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".