Abstract 1201 "Isolation and Characterization of Bacteriophages Infecting Metal Iron-reducing Bacterium Shewanella oneidensis MR-1"
Bibliographic record
Abstract
Bacterial viruses, or bacteriophages (phages) represent the most abundant biological entities on earth. Phages are known to attack specific bacteria, but their specificity of interaction is deeply under-characterized and focused only on model bacterial systems. The goal of this project was to isolate and characterize double-stranded DNA phages for Shewanella oneidensis MR-1, a bacterium with applications in electro-biotechnology. Toward this goal, it was enriched with more than 35 environmental samples with Shewanella oneidensis MR-1 and carried out plaque (a zone of clearance) assays to isolate 10 different phages. Then serial dilutions and spot assays were performed to estimate phage numbers in plaque forming units and extracted their genomic DNA by the Promega Wizard method. The phage samples are being prepared for image analysis by Transmission Electron Microscope (TEM) and genome sequencing using Illumina genome sequencing. Finally, assays are being planned to identify phage-specific host receptors using barcoded loss-of-function mutant libraries. By studying S. oneidensis MR-1 interaction with diverse phages, we will gain insight into the host genetic factors that render bacteria susceptible or resistant to killing by specific phages.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".