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Appraisal of Indoor Microbial Pollutants at University Research Labs and Offices under Mediterranean Climate

2020· article· en· W3045200205 on OpenAlexaff
Eliama Abed, Wesam Al Madhoun, Abdelraouf A. Elmanama, H. Kim, Xiao Xu, Faizah Che Ros

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicIndoor Air Quality and Microbial Exposure
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsIndoor airEnvironmental scienceToxicologyAir pollutantsVeterinary medicineGeographyBiologyEnvironmental engineeringAir pollutionEcologyMedicine

Abstract

fetched live from OpenAlex

Abstract Bacteria and fungi grow indoors when sufficient moisture is available. This study aims to determine the total viable bacterial count and fungi levels and to compare these levels among various science teaching laboratories and staff offices in Gaza city universities. A cross-sectional prospective study was conducted, sixty-five (65) air samples were collected from three local universities using Air Sampler. Auto Ranging Multimeter was used to record humidity and temperature. Air samples with counts of bacteria or fungi more than 500 Colony forming units “CFU”/m 3 were considered polluted according to the world health organization (WHO) standards. Among the 65 samples from the three universities, 48 sample (73.8%) had more than 500 colonies/100L. In term of fungal concentration, nine samples (13.8%) exceeded the WHO standards of 500 CFU/m 3 . The highest percentage of bacterial load in air samples at the three universities (more than 500 CFU/m 3 ) was at IUG with 80.8% due to its old existence, more labs and lab activities. The lowest was at AQU with 61.5% due to less lab and lab activities. The highest percentage of fungal load in air samples at the three universities also were at IUG with 19.2 % and the lowest was at AQU with 7.7%.

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)0.001
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 score0.011
Threshold uncertainty score0.516

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.038
GPT teacher head0.257
Teacher spread0.219 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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