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Record W1986855043 · doi:10.1139/w05-131

Effects of amoebae on the growth of microbes isolated from moisture-damaged buildings

2006· article· en· W1986855043 on OpenAlexvenueno aff
Terhi Yli‐Pirilä, Jaana Kusnetsov, Maija‐Riitta Hirvonen, Markku Seuri, Aino Nevalainen

Bibliographic record

VenueCanadian Journal of Microbiology · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLegionella and Acanthamoeba research
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyPenicilliumMicrobiologyBacteriaPseudomonas fluorescensAspergillus versicolorBacillus cereusMicroorganismBacillus (shape)AspergillusBotany

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.004
GPT teacher head0.194
Teacher spread0.190 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations20
Published2006
Admission routes1
Has abstractyes

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