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Record W4213296771 · doi:10.1128/aem.02061-21

Citizen Science Surveillance of Triazole-Resistant <i>Aspergillus fumigatus</i> in United Kingdom Residential Garden Soils

2022· article· en· W4213296771 on OpenAlexfundno aff
Jennifer Shelton, Roseanna Collins, Christopher B. Uzzell, Asmaa Alghamdi, Paul S. Dyer, Andrew C. Singer, Matthew C. Fisher

Bibliographic record

VenueApplied and Environmental Microbiology · 2022
Typearticle
Languageen
FieldMedicine
TopicAntifungal resistance and susceptibility
Canadian institutionsnot available
FundersNatural Environment Research CouncilWellcome TrustSight Research UKMedical Research CouncilCanadian Institute for Advanced ResearchAlbaha UniversityUK Research and Innovation
KeywordsAspergillus fumigatusCitizen scienceSoil waterBiologyEnvironmental healthMicrobiologyEcologyMedicineBotany

Abstract

fetched live from OpenAlex

The findings presented here highlight compost as a potential health hazard to individuals with predisposing factors to A. fumigatus lung infections and as a potential health hazard to immunocompetent individuals who could be exposed to sufficiently high numbers of spores to develop infection. Furthermore, we found that 14% of A. fumigatus isolates in garden soils were resistant to an agricultural triazole, which confers cross-resistance to medical triazoles used to treat A. fumigatus lung infections. This raises the question of whether compost bags should carry additional health warnings regarding inhalation of A. fumigatus spores, whether individuals should be advised to wear facemasks while handling compost, or whether commercial producers should be responsible for sterilizing compost before shipping. The findings support increasing public awareness of the hazard posed by compost and investigating measures that can be taken to reduce the exposure risk.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.100
Threshold uncertainty score0.200

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.225
Teacher spread0.214 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations43
Published2022
Admission routes1
Has abstractyes

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