Identification of synergistic interactions among microorganisms in biofilms by digital image analysis
Bibliographic record
Abstract
Digital image analysis showed that reductions in biofilm plating efficiency were due to the loss of protection provided by two benzoate-degrading strains of Pseudomonas fluorescens. This loss in protection was due to the spatial separation of the protective organisms from benzoate-sensitive organisms during the dilution process. Communities were cultivated in flow cells irrigated with trypticase soy broth. When the effluent from these flow cells was plated on 0.15% benzoic acid, satellite colonies formed only in the vicinity of primary colonies. A digital image analysis procedure was developed to measure the size and spatial distribution of these satellites as a function of distance from the primary colony. The size of satellites served as a measure of growth, and the number per unit area served as a measure of survival. At the three dilutions tested, the size and concentration of satellite colonies varied inversely with distance from the primary colonies. When these measurements were plotted, the slopes were used to quantify the effect of bacterial association on the growth and survivability of the satellites. In the absence of the primary colonies, satellites grew in axenic culture only at low benzoate concentrations. Thus benzoate-degrading organisms are capable of creating a protective microenvironment for other members of biofilm communities.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".