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Record W3185257208 · doi:10.1016/j.jmb.2021.167177

Tetravalent SARS-CoV-2 Neutralizing Antibodies Show Enhanced Potency and Resistance to Escape Mutations

2021· article· en· W3185257208 on OpenAlexafffund
Shane Miersch, Zhijie Li, Reza Saberianfar, Mart Ustav, James Brett Case, Levi L. Blazer, Chao Chen, Wei Ye, Alevtina Pavlenco, Maryna Gorelik, Julia Garcia Perez, Suryasree Subramania, Serena Singh, Lynda Ploder, Safder S. Ganaie, Rita E. Chen, Daisy W. Leung, Pier Paolo Pandolfi, Giuseppe Novelli, Giulia Matusali, Francesca Colavita, Maria Rosaria Capobianchi, Suresh Kumar Jain, Jyoti Gupta, Gaya K. Amarasinghe, Michael Diamond, James M. Rini, Sachdev S. Sidhu

Bibliographic record

VenueJournal of Molecular Biology · 2021
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsCentre for Disability Prevention and RehabilitationUniversity of Toronto
FundersNational Institute of Allergy and Infectious DiseasesMeso Scale DiagnosticsFondazione RomaRegione LazioCanadian Institutes of Health ResearchDefense Advanced Research Projects AgencyFoundation for Angelman Syndrome TherapeuticsNational Institutes of Health
KeywordsAvidityBivalent (engine)PotencyVirologyAntibodyNeutralizing antibodyChemistrySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)MutantCoronavirus disease 2019 (COVID-19)BiologyVirusIn vitroBiochemistryImmunologyMedicineInfectious disease (medical specialty)Gene

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.033
GPT teacher head0.372
Teacher spread0.339 · 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
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

Citations43
Published2021
Admission routes2
Has abstractno

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