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Record W3156308097 · doi:10.1093/infdis/jiab199

Introduction, Transmission Dynamics, and Fate of Early Severe Acute Respiratory Syndrome Coronavirus 2 Lineages in Santa Clara County, California

2021· article· en· W3156308097 on OpenAlexfundno aff
Elsa Villarino, Xianding Deng, Carol A. Kemper, Michelle A. Jorden, Brandon Bonin, Sarah L. Rudman, George S. Han, Guixia Yu, Candace Wang, Scot Federman, Brian Bushnell, Debra A. Wadford, Wen Lin, Ying Tao, Clinton R. Paden, Julu Bhatnagar, Tara MacCannell, Suxiang Tong, Joshua Batson, Charles Y. Chiu

Bibliographic record

VenueThe Journal of Infectious Diseases · 2021
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsnot available
FundersNational Institute of Allergy and Infectious DiseasesCanadian Institutes of Health ResearchNational Institutes of HealthInnovative Genomics Institute
KeywordsCoronavirusCoronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Transmission (telecommunications)PandemicBiologyVirologyMedicineDiseaseInternal medicineInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

We combined viral genome sequencing with contact tracing to investigate introduction and evolution of severe acute respiratory syndrome coronavirus 2 lineages in Santa Clara County, California, from 27 January to 21 March 2020. From 558 persons with coronavirus disease 2019, 101 genomes from 143 available clinical samples comprised 17 lineages, including SCC1 (n = 41), WA1 (n = 9; including the first 2 reported deaths in the United States, with postmortem diagnosis), D614G (n = 4), ancestral Wuhan Hu-1 (n = 21), and 13 others (n = 26). Public health intervention may have curtailed the persistence of lineages that appeared transiently during February and March. By August, only D614G lineages introduced after 21 March were circulating in Santa Clara County.

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 score0.329
Threshold uncertainty score0.654

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
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 head0.017
GPT teacher head0.310
Teacher spread0.294 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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