Genomic approaches to characterizing and reducing antimicrobial resistance in beef cattle production systems
Bibliographic record
Abstract
Antimicrobial resistance (AMR) is a global health threat, and a standstill in the discovery and design of new antibiotics has been linked to the growing number of human deaths attributed to AMR infections. Intensive beef production utilizes antimicrobials to promote health and growth efficiency. To understand the magnitude and risk of AMR in beef production, it is important to assess the prevalence and diversity of antimicrobial resistant genes (ARGs) within microbial populations. Antimicrobial resistant bacteria are traditionally identified by isolation and growth in the presence of selective antibiotics. Whole-genome, metagenomic, and RNA sequencing provide new avenues to detect and identify novel ARGs in both culturable and unculturable bacterial communities. Some of these approaches place ARGs within the context of mobile genetic elements, gauging their likelihood of transfer across genomes. Genomics can also mitigate AMR, contributing to rational drug design or the development of alternatives to antimicrobials such as vaccines and probiotics. RNA-seq-based transcriptomics and Tn-seq may provide new ways to examine mechanisms that promote or prevent AMR. Finally, clustered regularly interspaced short palindromic repeats (CRISPR) – Cas gene editing could directly reduce AMR by killing AMR-resistant bacteria without harming beneficial bacteria. Together, these technologies may provide new opportunities to identify, quantify, and mitigate AMR while developing alternatives to antimicrobials for beef production.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".