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Record W3198239613 · doi:10.1099/acmi.0.000257

Performance comparison of micro-neutralization assays based on surrogate SARS-CoV-2 and WT SARS-CoV-2 in assessing virus-neutralizing capacity of anti-SARS-CoV-2 antibodies

2021· article· en· W3198239613 on OpenAlex
Inna Sekirov, Martin Petric, Erin Carruthers, David S. Lawrence, Tamara Pidduck, Jesse Kustra, Jonathan Laley, Min-Kuang Lee, Navdeep Chahil, Annie Mak, Paul N. Levett, Emelissa J. Mendoza, Heidi Wood, Mike Drebot, Mel Krajden, Muhammad Morshed

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAccess Microbiology · 2021
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsBC Centre for Disease ControlUniversity of British Columbia
Fundersnot available
KeywordsNeutralizationVirologySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)AntibodyNeutralizing antibodyCoronavirusVirusCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakCoronaviridaeBiologyMedicineImmunologyOutbreakInfectious disease (medical specialty)Disease

Abstract

fetched live from OpenAlex

We compared neutralization assays using either the wild-type severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus or surrogate neutralization markers, using characterized sera. We found the results of the neutralization assays 75 % concordant overall and 80 % concordant for samples with high antibody levels. This demonstrates that commercial surrogate SARS-CoV-2 assays offer the potential to assess anti-SARS-CoV-2 antibodies' neutralizing capacity outside CL-3 laboratory containment.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.343
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.118
GPT teacher head0.407
Teacher spread0.289 · 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