Field monitoring of plant-growth-promoting rhizobacteria by colony immunoblotting
Bibliographic record
Abstract
Inoculant plant-growth-promoting bacteria are emerging as an important component of sustainable agriculture. There is a need to develop inexpensive methods for enumerating these organisms after their application in the field, to better understand their survival and impacts on yields. Immunoblotting is one potential method to measure viable cells, but the high cost of the conventionally used nylon membranes makes this method prohibitive. In this study, less expensive alternative materials such as filter papers, glossy photo papers, and transparencies for the purpose of colony immunoblotting were evaluated and the best substance was chosen for further studies. Whatman filter paper No. 541 combined with a 0.01 mol·L(-1) H(2)SO(4) rinsing step gave similar results to nylon membranes but <20% of the overall cost of the original colony immunoblotting assay. The application of the modified immunoblot method was tested on nonsterile clay soil samples that were spiked with high numbers (>10(7) CFU·g(-1)) of the plant-growth-promoting bacteria Pseudomonas fluorescens , Azospirillum brasilense , or Rhizobium leguminosarum . The modified protocol allowed the identification and recovery of over 50% of the inoculated cells of all three strains, amidst a background of the native soil microflora. Subsequently, the survival of P. fluorescens was successfully monitored for several months after application to field-grown rice at Jerilderie, New South Wales, Australia, thus validating the procedure.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
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".