Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This chapter provides an overview of plasmid classification systems and then describes the various mechanisms of plasmid-mediated resistance to antibacterial agents. The major pathways for the evolution of bacterial resistance plasmids are discussed. In general, plasmid-mediated resistance to antibacterials is a result of three major mechanisms: (i) destruction or modification of the antibacterial agents, (ii) prevention of the antibacterial agent from reaching its target in the bacterial cell, and (iii) production of an altered bacterial target. Resistance to tetracyclines, macrolides, glycopeptides, and quinolones are examples of this type of resistance. The majority of the approximately 60 known gene cassettes code for antibiotic resistance determinants. Genes in integrons have been found to encode resistance to a wide variety of antibiotics, including aminoglycosides, chloramphenicol, erythromycin, and the β-lactams. The aadA1 gene cassette (encoding spectinomycin resistance), carried by Tn21, is one of the most widespread resistance genes. Although the epidemiology of integrons has only recently been investigated, several reports have emphasized the importance of integrons in the dissemination of antibiotic resistance. IncHI1 plasmids are representative of antibiotic resistance plasmids that play a central role in the emergence and reemergence of bacterial pathogens. The chapter highlights the dynamic processes involved in plasmid evolution to acquire resistance markers. Transposons, integrons, conjugation, and other mechanisms of resistance spread are all employed by pathogens to respond to continued antibiotic usage in the clinic and in the environment.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it