Phage Therapy Approaches to Reducing Pathogen Persistence and Transmission in Animal Production Environments: Opportunities and Challenges
Bibliographic record
Abstract
One of the major challenges to current global food production and food security is the presence of antibiotic-resistant bacteria in animals (ruminants, poultry, swine) from which foods of animal origin are produced. Foodborne diseases significantly impact public health globally, with the World Health Organization (WHO) estimating that 1 in 10 people, or approximately 600 million people worldwide, are sickened and 420,000 die annually from foodborne illnesses (1). There is concern that many foodborne bacterial pathogens are either resistant or increasing their resistance to antimicrobials commonly used for medical treatment. For example, the Centers for Disease Control and Prevention reported that in 2013, the percentage of human Campylobacter jejuni isolates with macrolide resistance increased from 1.8% in 2012 to 2.2% in 2013, and from 9.0% in 2012 to 17.6% among Campylobacter coli isolates (2). In addition, the percentage of human Salmonella ser. I 4,[5],12:i:- isolates with resistance to ampicillin, streptomycin, sulfonamide, and tetracycline continued to increase, from 17% in 2010 to 45.5% in 2013 (2). Campylobacter spp. (845,024 cases per year) and nontyphoidal Salmonella spp. (1,027,561 cases per year) are the two most prevalent causes of foodborne illness in the United States, accounting for 51% of annual foodborne illnesses due to known bacterial agents (3) and highlighting the fact that an increasing number of foodborne illnesses are becoming more difficult to treat with antibiotics.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".