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Record W3143208961 · doi:10.1111/1348-0421.12883

Multiple clades of SARS‐CoV‐2 were introduced to Thailand during the first quarter of 2020

2021· article· en· W3143208961 on OpenAlexaboutno aff
Rome Buathong, Walairat Chaifoo, Sopon Iamsirithaworn, Supaporn Wacharapluesadee, Yutthana Joyjinda, Apaporn Rodpan, Weenassarin Ampoot, Opass Putcharoen, Leilani Paitoonpong, Gompol Suwanpimolkul, Watsamon Jantarabenjakul, Sininat Petcharat, Saowalak Bunprakob, Siriporn Ghai, Wisit Prasithsirikul, Anek Mungaomklang, Tanarak Plipat, Thiravat Hemachudha

Bibliographic record

VenueMicrobiology and Immunology · 2021
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsnot available
FundersDefense Threat Reduction AgencyNational Research Council of ThailandKing Chulalongkorn Memorial HospitalYale University
KeywordsLineage (genetic)CladeSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)BiologyCoronavirusCoronavirus disease 2019 (COVID-19)Quarter (Canadian coin)Virology2019-20 coronavirus outbreakPandemicVirusChinaDiseasePhylogeneticsOutbreakInfectious disease (medical specialty)MedicineGeneticsGeneInternal medicineHistory

Abstract

fetched live from OpenAlex

In early January 2020, Thailand became the first country where a coronavirus disease 2019 (COVID-19) patient was identified outside China. In this study, 23 whole genomes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from patients who were hospitalized from January to March 2020 were analyzed, along with their travel histories. Six lineages were identified including A, A.6, B, B.1, B.1.8, and B.58, among which lineage A.6 was dominant. Seven patients were from China who traveled to Thailand in January and early February. Five of them were infected with the B lineage virus, and the other two cases were infected with different lineages including A and A.6. These findings present clear evidence of the early introduction of diverse SARS-CoV-2 clades in Thailand.

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)0.000
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 score0.064
Threshold uncertainty score0.348

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.015
GPT teacher head0.280
Teacher spread0.265 · 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

Citations9
Published2021
Admission routes1
Has abstractyes

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