Bibliographic record
Abstract
Antibiotics remain one of our most important pharmacological tools for the control of infectious disease. However, unlike most other drugs, the use of antibiotics selects for resistant organisms and erodes their clinical utility. Resistance can emerge within populations of bacteria by mutation and be retained by subsequent selection or by the acquisition of resistance elements laterally from other organisms. The source of these resistance genes is only now being understood. The evidence supports a large bacterial resistome-the collection of all resistance genes and their precursors in both pathogenic and nonpathogenic bacteria. These genes have arisen by various means including self-protection in the case of antibiotic producers, transport of small molecules for various reasons including nutrition and detoxification of noxious chemicals, and to accomplish other goals, such as metabolism, and demonstrate serendipitous selectivity for antibiotics. Regardless of their origins, resistance genes can rapidly move through bacterial populations and emerge in pathogenic bacteria. Understanding the processes that contribute to the evolution and selection of resistance is essential to mange current stocks of antibiotics and develop new ones. PMID: 23090593 Funding information This work was supported by: Canadian Institutes of Health Research, Canada
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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.016 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.190 | 0.091 |
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".