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Record W4392375846 · doi:10.1093/jtm/taae040

Wastewater-based epidemiological surveillance of SARS-CoV-2 new variants BA.2.86 and offspring JN.1 in South and Southeast Asia

2024· article· en· W4392375846 on OpenAlexaff
Dhammika Leshan Wannigama, Mohan Amarasiri, Phatthranit Phattharapornjaroen, Cameron Hurst, Charin Modchang, Sudarat Chadsuthi, Suparinthon Anupong, Kazuhiko Miyanaga, Longzhu Cui, W. K. C. P. Werawatte, Seyed Mohammad Ali Hosseini Rad, Stefan Fernandez, Angkana T. Huang, Porames Vatanaprasan, Thammakorn Saethang, Sirirat Luk-in, Robin James Storer, Puey Ounjai, Ratana Tacharoenmuang, Naveen Kumar Devanga Ragupathi, Phitsanuruk Kanthawee, Bernadina Cynthia, John Jefferson V. Besa, Asada Leelahavanichkul, Talerngsak Kanjanabuch, Paul G. Higgins, Asuka Nanbo, Anthony Kicic, Andrew C. Singer, Tanittha Chatsuwan, Sam Trowsdale, Takashi Furukawa, Kazunari Sei, Daisuke Sano, Hitoshi Ishikawa, Kenji Shibuya, Aisha Khatib, Shuichi Abe, Parichart Hongsing

Bibliographic record

VenueJournal of Travel Medicine · 2024
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 detection and testing
Canadian institutionsUniversity of Toronto
FundersCentre of Excellence in Mathematics, Mahidol UniversityChulalongkorn UniversityUniversity of Western Australia
KeywordsMedicineSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)EpidemiologyCoronavirus disease 2019 (COVID-19)VirologySoutheast asia2019-20 coronavirus outbreakCoronavirus InfectionsEnvironmental healthVeterinary medicineInternal medicineInfectious disease (medical specialty)Ancient historyOutbreakDisease

Abstract

fetched live from OpenAlex

Discover the shifting landscape of SARS-CoV-2 variants from October to December 2023, with JN.1 dominating South and Southeast Asia wastewater samples, increasing from <10% to >90%. Experience the dynamic evolution of viral strains in this period.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.026
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.086
GPT teacher head0.335
Teacher spread0.249 · 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 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

Citations11
Published2024
Admission routes1
Has abstractyes

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