Bacteriophage Effectiveness for Biocontrol of Foodborne Pathogens Evaluated via High-Throughput Settings
Bibliographic record
Abstract
Bacterial pathogens continually challenge food safety systems worldwide. With increasing concerns about the emergence of heat- and sanitizer-resistant bacteria, novel antibacterial agents, are urgently needed. A bacteriophage-based biocontrol strategy is the therapeutic use of phages to control bacterial pathogens in agricultural settings. Phage biocontrol is increasingly accepted as a sustainable technology, effective at decontaminating foodborne pathogens. To ensure effective biocontrol outcomes, systematic screening of phage combinations against targeted bacteria under required environmental conditions is crucial. Antibacterial efficacy of phage cocktails may be affected by phage genera and combination, targeted bacterial strains, the multiplicity of infection, temperature, and time. To formulate a phage cocktail with superior efficacy, the proposed method was to systematically evaluate the effectiveness of individual phages and phage cocktails in killing foodborne bacterial pathogens under targeted conditions. Bacterial killing efficacy was monitored by measuring optical density at desired temperatures and durations. Superior phage efficacy was determined by complete inhibition of bacterial growth. The proposed method is a robust, evidence-based approach to facilitate formulating phage cocktails with superior antibacterial efficacy.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".