The Best-Practice Organism for Single-Species Studies of Antimicrobial Efficacy against Biofilms Is Pseudomonas aeruginosa
Bibliographic record
Abstract
As potable water scarcity increases across the globe; it is imperative to identify energy and cost-effective processes for producing drinking-water from non-traditional sources. One established method is desalination of brackish and seawater via reverse osmosis (RO). However, the buildup of microorganisms at the water-membrane interface, known as biofouling, clogs RO membranes over time, increasing energy requirements and cost. To investigate biofouling mitigation methods, studies tend to focus on single-species biofilms; choice of organism is crucial to producing useful results. To determine a best-practice organism for studying antimicrobial treatment of biofilms, with specific interest in biofouling of RO membranes, we answered the following two questions, each via its own semi-systematic review: 1. Which organisms are commonly used to test antimicrobial efficacy against biofilms on RO membranes? 2. Which organisms are commonly identified via genetic analysis in biofilms on RO membranes? We then critically review the results of two semi-systematic reviews to identify pioneer organisms from the listed species. We focus on pioneer organisms because they initiate biofilm formation, therefore, inhibiting these organisms specifically may limit biofilm formation in the first place. Based on the analysis of the results, we recommend utilizing Pseudomonas aeruginosa for future single-species studies focused on biofilm treatment including, but not limited to, biofouling of RO membranes.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".