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Record W3204721907 · doi:10.1039/d1ew00539a

Development of a rapid pre-concentration protocol and a magnetic beads-based RNA extraction method for SARS-CoV-2 detection in raw municipal wastewater

2021· article· en· W3204721907 on OpenAlexafffund
Ana L. Parra Guardado, Crystal L. Sweeney, Emalie K. Hayes, Benjamin F. Trueman, Yannan Huang, Rob Jamieson, Jennie L. Rand, Graham A. Gagnon, Amina K. Stoddart

Bibliographic record

VenueEnvironmental Science Water Research & Technology · 2021
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 detection and testing
Canadian institutionsAcadia UniversityDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaDalhousie UniversityCanadian Water NetworkUniversity of Alberta
KeywordsExtraction (chemistry)WastewaterMagnetic beadChromatographyRNA extractionRNARaw materialMagnetic nanoparticlesChemistryMaterials scienceWaste managementNanotechnologyNanoparticleBiochemistryEngineering

Abstract

fetched live from OpenAlex

We demonstrate the application of a rapid pre-concentration protocol and a magnetic bead-based RNA extraction method for the detection of SARS-CoV-2 RNA from raw wastewater without the need for extensive sample treatment.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.066
GPT teacher head0.402
Teacher spread0.336 · 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
GenreMethods

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

Citations25
Published2021
Admission routes2
Has abstractyes

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Same venueEnvironmental Science Water Research & TechnologySame topicSARS-CoV-2 detection and testingFrench-language works237,207