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Record W4323350685 · doi:10.1101/2023.03.03.23286750

Detection of SARS-CoV-2 in Schools Using Built Environment Testing in Ottawa, Canada: A Multi-Facility Prospective Surveillance Study

2023· preprint· en· W4323350685 on OpenAlexaffabout
Nisha Thampi, Tasha Burhunduli, Jamie Strain, Ashley Raudanskis, Jason Moggridge, Aaron Hinz, Evgueni Doukhanine, Castellani, Fralick, Rees Kassen, Janine McCready, Caroline Nott, Wong, Derek R. MacFadden

Bibliographic record

VenuemedRxiv · 2023
Typepreprint
Languageen
FieldMedicine
TopicInfection Control and Ventilation
Canadian institutionsOttawa HospitalNOSM UniversityLunenfeld-Tanenbaum Research InstituteChildren's Hospital of Eastern OntarioCarleton UniversitySinai Health SystemToronto East General HospitalAgricultural Research Institute of OntarioSault Area HospitalUniversity of Ottawa
Fundersnot available
KeywordsAbsenteeismSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Coronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakMedicineEnvironmental healthTest (biology)VirologyPsychologyInternal medicine

Abstract

fetched live from OpenAlex

Abstract Classroom and staffroom floor swabs across six elementary schools in Ottawa, Canada were tested for SARS-CoV-2. Schools in neighbourhoods with historically elevated COVID-19 burden had lower environmental swab positivity. Environmental test positivity did not correlate with student grade groups, school-level absenteeism, pediatric COVID-19-related hospitalizations, or community SARS-CoV-2 wastewater levels. Summary Environmental SARS-CoV-2 sampling was performed in six schools in Ottawa, Canada. The percentage of floor swabs detecting SARS-CoV2 was not correlated with absenteeism, pediatric hospitalizations, or wastewater data. Schools in neighbourhoods with previously elevated COVID-19 rates had lower test positivity.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.183
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.092
GPT teacher head0.321
Teacher spread0.229 · 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 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

Citations1
Published2023
Admission routes2
Has abstractyes

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