High-throughput Method for Observing Motility Phenotypes in <em>Pseudomonas aeruginosa</em>
Bibliographic record
Abstract
Motility behaviors often play a significant role in the ability of a bacterium to exploit the resources available in its environment. This is particularly true for the versatile pathogen Pseudomonas aeruginosa, which can exhibit diverse types of motilities, including swarming and twitching, which are important pathogenic traits that contribute to surface colonization, biofilm formation, and evasion of host defenses. This manuscript presents a high-throughput motility protocol to study the motility behaviors of P. aeruginosa. The protocol allows simultaneous testing of multiple strains of P. aeruginosa from a genome-wide mutant library, for instance, to identify and analyze the genetic factors involved in its motility. The approach offers the possibility to study motility in a comprehensive manner and insights into the molecular mechanisms underlying P. aeruginosa's motility. The protocol described here can also be modified to accommodate different types of motility assays as well as other bacterial species, thus providing a powerful platform for advancing the understanding of bacterial behavior in various contexts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".