Bibliographic record
Abstract
ABSTRACT Staphylococci, and in particular Staphylococcus aureus , cause an extensive variety of infections in a range of hosts. The comprehensive analysis of staphylococcal genomes reveals mechanisms controlling the organism’s biology, pathobiology, and dissemination. Whole-genome sequencing technologies led to a quantum leap in our understanding of bacterial genomes. The recent cost reduction of sequencing has resulted in unprecedented volumes of genomic information about S. aureus , one of the most sequenced bacterial species. Collecting, comparing, and interpreting big data is challenging, but fascinating insights have emerged. For example, it is becoming clearer which selective pressures staphylococci face in their habitats and which mechanisms allow this pathogen to adapt, survive, and spread. A key theme is the constant evolution of staphylococci as they alter their genome, exchange DNA, and adapt to new environments, leading to the emergence of increasingly successful, antibiotic-resistant, immune-evading, and host-adapted colonizers and pathogens. This article introduces the structure of staphylococcal genomes, details how genomes vary between strains, outlines the mechanisms of genetic variation, and describes the features of successful clones.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".