Broad-spectrum antibacterial properties of a Pseudomonas field isolate and potential rhizomicrobiome implications
Bibliographic record
Abstract
Plant growth promoting bacteria (PGPB) such as Pseudomonas have been widely acclaimed for their roles in sustainable food production and consumption of pollutants in contaminated soils. Yet, we still have much to learn about how dynamics of highly diverse microbial communities are influenced by PGPBs capable of antibacterial activity. This article characterizes the antimicrobial profile of an Indiana Pseudomonas field isolate. To our knowledge, this work is the first to demonstrate Pseudomonas-induced inhibition of the PGPB Serratia marcescens, as well as Pseudomonas-induced inhibition and/or color change of Kocuria rhizophila. These data raise intriguing questions about how best to maximize the efficacy of biofertilizers containing multiple different organisms while minimizing unintended disruption of soil microbiomes. In addition, similar to previously published Pseudomonas strains, our isolate inhibited growth of clinically relevant bacteria such as Staphylococcus, Mycobacterium, and Sarcina. Future studies thus should investigate whether Pseudomonas-derived antibiotics could have novel applications in treating opportunistic Serratia, Kocuria, or Sarcina human infections.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Characterization of antibacterial activity of a Pseudomonas isolate; microbiology.
This study investigates antibacterial activity and soil microbiomes rather than research itself.
Microbiology study of a Pseudomonas isolate and antibacterial activity, not research as object.
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 | 0.000 |
| 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".