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 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".