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Record W4384694741 · doi:10.22215/etd/2023-15531

Monitoring SARS-CoV-2 in Municipal Wastewater and Correlation of Results with Covid-19 Cases from Five Municipalities in Ontario, Canada

2023· dissertation· en· W4384694741 on OpenAlexfundaboutno aff
Gabriela Carolina Jimenez Pabon

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldMedicine
TopicSARS-CoV-2 detection and testing
Canadian institutionsnot available
FundersMinistère de l’Environnement, de la Protection de la nature et des ParcsMinistry of Environment
KeywordsWastewaterCoronavirus disease 2019 (COVID-19)TurbiditySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Environmental scienceSewage treatmentSignal strengthVeterinary medicineEnvironmental engineeringBiologyEcologyMedicineInternal medicineEngineeringTelecommunications

Abstract

fetched live from OpenAlex

This study investigated the wastewater SARS-CoV-2 RNA signal correlations with community COVID-19 cases and compared results from five municipal wastewater treatment plants in Ontario, Canada, monitored from October 2021 to April 2022. 24-hour composite wastewater samples were collected three times a week at each site and analyzed for SARS-CoV-2, using pepper mild mottle virus (PMMoV) as the main inhibition control. Viral copies were quantified using RT-qPCR. Temperature, pH, turbidity, TS, VS, and UV-Vis scans were measured for all samples. Viral RNA signal was normalized by PMMoV, TS and VS. Correlations between wastewater SARS-CoV-2 RNA signal and COVID-19 case numbers were statistically significant at all sites, showing stronger correlations when comparing normalized data. Results from the five sites were ranked based on the strength of their statistical correlations, with differences potentially attributed to sewer and site characteristics such as populations, sewer sizes or sewer types (combined or separate).

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.321
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.070
GPT teacher head0.323
Teacher spread0.252 · 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.

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

Citations0
Published2023
Admission routes2
Has abstractyes

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