Bibliographic record
Abstract
Bacteria owe their ability to thrive in diverse and ever-changing conditions to their extraordinary faculty of adaptation. They have developed many strategies to adjust to new environments. These mechanisms include random modifications within their genome, such as point mutation, duplication, deletion, insertion, and acquisition of new DNA (e.g., lateral gene transfer). These multiple events generate a heterogeneous microbial population containing numerous novel phenotypes. Whenever the environment changes, a subpopulation more apt to survive in these new conditions emerges, thus allowing bacteria to thrive, for example, by acquiring resistance to antibiotics. However, as the intensity, duration, and nature of stress are extremely variable, the optimal response to new environmental conditions may be unpredictable. The means by which bacteria either respond to stress, such as exposure to toxic/inhibitory compounds or starvation, or avoid detection by the host's immune system are crucial for their survival. The spontaneous mutation rate is usually insufficient for allowing an efficient adaptation to these changes. However, certain bacterial populations contain hypermutator strains exhibiting highly increased rates of spontaneous mutations, therefore promoting adaptation to changing environments (Taddei et al., 1997). Nevertheless, this benefit may disappear once adaptation is achieved because the evolved genotype may have accumulated irreversible mutations that are detrimental in other conditions (Giraud et al., 2001a, 2001b). Alternatively, bacteria have developed adaptation strategies based on DNA rearrangement events restricted to specific genomic regions. These defined loci allow bacteria to generate an array of phenotypic variants, whereas minimizing detrimental effects of random mutations on fitness.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".