Effects of amoebae on the growth of microbes isolated from moisture-damaged buildings
Bibliographic record
Abstract
Dampness, moisture, and mold in buildings are associated with adverse health outcomes. In addition to fungi and bacteria, amoebae have been found in moisture-damaged building materials. Amoebae and a growing list of bacteria have been shown to have mutual effects on each other's growth, but the interactions between amoebae and microbes common in moisture-damaged buildings have not been reported. We co-cultivated the amoeba Acanthamoeba polyphaga with bacteria and fungi isolated from moisture-damaged buildings in laboratory conditions for up to 28 days. The microbes selected were the bacteria Streptomyces californicus, Bacillus cereus, and Pseudomonas fluorescens, and the fungi Stachybotrys chartarum, Aspergillus versicolor, and Penicillium spinulosum. Fungi and bacteria generally benefited from the presence of the amoebae, whereas the growth of amoebae was hindered by Streptomyces californicus, Stachybotrys chartarum, and Bacillus cereus. Pseudomonas fluorescens slightly enhanced amoebae viability. Amoebae were indifferent to the presence of Aspergillus versicolor and Penicillium spinulosum. Thus, our results show that amoebae can alter the survival and growth of some microbes in moisture-damaged buildings.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".