Methods to Determine Fitness in Bacteria
Bibliographic record
Abstract
Acquisition of antibiotic resistance may be associated with a physiological cost for the bacterium. Determination of growth rate and generation time is often used to measure fitness costs associated with antibiotic resistance. However, fitness costs may be small and difficult to quantify and multiple models are required. Available in vitro models that can be used to measure fitness include quantification of biofilm growth, survival in water, resistance to drying, and measurement of planktonic growth rates. The use of growth curve techniques to determine generation time is laborious, time-consuming, and can introduce sampling error. We have described the use of a semi-automated liquid culture system to estimate generation time in Burkholderia cepacia complex bacteria. We have also used the BacT/ALERT system to determine generation time and enumerate bacterial numbers in Mycobacterium tuberculosis. We describe methods for measuring biofilm growth and environmental survival in Burkholderia cepacia complex bacteria. These methods can be adapted for use with other organisms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".