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Record W3208204797 · doi:10.1038/s41598-021-01576-w

Author Correction: Predicting mammalian species at risk of being infected by SARS-CoV-2 from an ACE2 perspective

2021· erratum· en· W3208204797 on OpenAlexaff
Yulong Wei, Parisa Aris, Heba Farookhi, Xuhua Xia

Bibliographic record

VenueScientific Reports · 2021
Typeerratum
Languageen
FieldMedicine
TopicZoonotic diseases and public health
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsPerspective (graphical)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Coronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSars virusBetacoronavirusCoronavirus InfectionsComputational biologyVirologyBiologyBioinformaticsMedicineComputer scienceInternal medicineArtificial intelligenceDiseaseOutbreak

Abstract

fetched live from OpenAlex

The original version of this Article contained an error in the Results section, under the subheading ‘Key binding sites on the human ACE2 receptor are most conserved by primates species and variably conserved in selected species belonging to eight other mammalian orders’, where the mink species “ Neovison vison ” was incorrectly given as “ Mustela lutreola ” due to an error in the GenBank SARS-CoV-2 genome records of the host species. It was brought to the attention of the Authors after the publication of this Article that GenBank records of mink-derived SARS-Cov-2 genomes consulted for the original Article (e.g., MT396266.1, MT457398.1, MT457399.1) were not correct at the time of its publication and remain incorrect at the time of publication of this correction notice.

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)0.003
metaresearch head score (Gemma)0.063
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.043
Threshold uncertainty score0.144

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.063
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0430.031

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 head0.023
GPT teacher head0.313
Teacher spread0.291 · 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 designNot applicable
Domainnot available
GenreOther

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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Same venueScientific ReportsSame topicZoonotic diseases and public healthFrench-language works237,207