Bibliographic record
Abstract
The oral streptococci are normally non-pathogenic residents of the human microflora. There is substantial evidence that these bacteria can, however, act as "genetic reservoirs" and transfer genetic information to transient bacteria as they make their way through the mouth, the principal entry point for a wide variety of bacteria. Examples that are of particular concern include the transfer of antibiotic resistance from oral streptococci to Streptococcus pneumoniae. The mechanisms that are used by oral streptococci to exchange genetic information are not well-understood, although several species are known to enter a physiological state of genetic competence. This state permits them to become capable of natural genetic transformation, facilitating the acquisition of foreign DNA from the external environment. The oral streptococci share many similarities with two closely related Gram-positive bacteria, S. pneumoniae and Bacillus subtilis. In these bacteria, the mechanisms of quorum-sensing, the development of competence, and DNA uptake and integration are well-characterized. Using this knowledge and the data available in genome databases allowed us to identify putative genes involved in these processes in the oral organism Streptococcus mutans. Models of competence development and genetic transformation in the oral streptococci and strategies to confirm these models are discussed. Future studies of competence in oral biofilms, the natural environment of oral streptococci, will be discussed.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".