Bibliographic record
Abstract
The discovery of antibiotics in the 1930s and their development for the treatment of infectious diseases represented a major advancement for medicine. There are several distinct biochemical mechanisms such as reduced permeability, active efflux and alteration of the drug target by which antibiotic resistance can arise, and these are discussed in the chapter. Although isoniazid (isonicotinic acid hydrazide, INH) is still one of the most effective antibiotics against tuberculosis, the number of INH- and other drug-resistant strains has increased dramatically. The evolution of resistance by mutation of an endogenous gene is more the exception than the rule, since the genetic basis of most antibiotic resistance among clinically significant bacteria is horizontal transfer. Although the incidence of mutator strains in environmental microbes and their possible roles in the tailoring of antibiotic resistance genes (or any horizontally transferred determinants, such as biodegradation clusters) is difficult to examine systematically in natural populations, their importance in the evolution of resistance should not be underestimated. There are three main mechanisms of horizontal gene transfer: transduction, transformation and conjugation. Conjugative DNA transfer is the principal mechanism for the dissemination of antibiotic resistance genes. Conjugative transposons are discrete elements that are normally integrated into a bacterial genome. The common association of multiresistant integrons (MRIs) with mobile DNA elements facilitates the transit of the resistance genes that have been amassed by integrons across phylogenetic boundaries and augments the impact of integrons on bacterial evolution.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".