Bibliographic record
Abstract
The ability of micro-organisms to develop resistance to the action of inhibitors has been known since the discovery of antimicrobial agents.The first reports were concerned with agents such as Salvarsan and Prontosil in the early 1900s; the phenomenon of 'fastness' to these agents was observed frequently during treatment with the drugs.Following the development of the sulfonamides in the 1940s and discovery of the 'true' antibiotics, those produced by fermentation such as penicillin, streptomycin, tetracycline, etc. in the 1950s, the study of antibiotic resistance and its mechanisms paralleled those concerned with mode of action.In the first place, the genetic characterization of a specific resistance mechanism was a critical step in defining antibiotic mode of action.The identification of a binding site or the accumulation of a biosynthetic intermediate provided important clues to the ways by which the antibiotics acted on microbes.To cite a specific case, mutants resistant to the aminoglycosides were early identified as alterations in ribosome structure, and these provided critical information about the structure of ribosomes and their functions in the translation process.The identification of these key components led to the characterization of antibiotic target sites within the ribosome structure: these have been confirmed by X-ray crystallographic studies (1).Similar analyses have revealed detailed molecular information on the targets for most classes of antibiotics.Interestingly, one phenotype that has never been adequately described is that of mutants that are dependent on the presence of antibiotics (such as streptomycin) for ribosome function in translation.Antibiotic resistance (AR) has become a field of research in its own right, and deciphering the widespread biochemical mechanisms involved have
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".