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Record W2146158310 · doi:10.1139/w04-002

Disinfection of selected <i>Aspergillus</i> spp. using ultraviolet germicidal irradiation

2004· article· en· W2146158310 on OpenAlexvenueno aff
Christopher F. Green, Pasquale V. Scarpino, Paul Jensen, Nancy J. Jensen, Shawn G. Gibbs

Bibliographic record

VenueCanadian Journal of Microbiology · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicIndoor Air Quality and Microbial Exposure
Canadian institutionsnot available
Fundersnot available
KeywordsAspergillus fumigatusSporeMicrobiologyAspergillus flavusPetri dishBiologyAspergillus

Abstract

fetched live from OpenAlex

AIMS: The efficacy of ultraviolet germicidal irradiation (UVGI) and the UVGI dose necessary to inactivate fungal spores on an agar surface for cultures of Aspergillus flavus and Aspergillus fumigatus were determined. METHODS AND RESULTS: A four-chambered UVGI testing unit with a 9-W, Phillips, low pressure, mercury UVGI lamp in each chamber was used in this study. An aperture was adjusted to provide 50, 100, 150, and 200 micro W/cm2 of uniform flux to the surfaces of the Petri dish, resulting in a total UVGI dose to the surface of the Petri dishes ranging from 12 to 96 mJ/cm2. The UVGI dose necessary to inactivate 90% of the A. flavus and A. fumigatus was 35 and 54 mJ/cm2, respectively. CONCLUSIONS: UVGI can be used to inactivate culturable fungal spores. Aspergillus flavus was more susceptible than A. fumigatus to UVGI. SIGNIFICANCE AND IMPACT OF THE STUDY: These results may not be directly correlated to the effect of UVGI on airborne fungal spores, but they indicate that current technology may not be efficacious as a supplement to ventilation unless it can provide higher doses of UVGI to kill spores traveling through the irradiated zone.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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)0.0010.000

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 head0.008
GPT teacher head0.204
Teacher spread0.196 · 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 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

Citations27
Published2004
Admission routes1
Has abstractyes

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