EXTENSIVE VARIATION IN NATURAL COMPETENCE IN<i>HAEMOPHILUS INFLUENZAE</i>
Bibliographic record
Abstract
The ability of some bacteria to take up and recombine DNA from the environment is an important evolutionary problem because its function is controversial; although populations may benefit in the long-term from the introduction of new alleles, cells also reap immediate benefits from the contribution of DNA to metabolism. To clarify how selection has acted, we have characterized competence in natural isolates of H. influenzae by measuring DNA uptake and transformation. Most of the 34 strains we tested became competent, but the amounts of DNA they took up and recombined varied more than 1000-fold. Differences in recombination were not due to sequence divergence and were only partly explained by differences in the amounts of DNA taken up. One strain was highly competent during log phase growth, unlike the reference strain Rd, but several strains did not develop competence under any of the tested conditions. Analysis of competence genes identified genetic defects in two poorly transformable strains. These results show that strains can differ considerably in the amount of DNA they take up and recombine, indicating that the benefit associated with competence is likely to vary in space and/or time.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".