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Record W3215546409 · doi:10.24018/ejmed.2021.3.6.1076

Vaccines for the COVID-19 α Variant: An Econometric Analysis

2021· article· en· W3215546409 on OpenAlexaff
James McIntosh

Bibliographic record

VenueEuropean Journal of Medical and Health Sciences · 2021
Typearticle
Languageen
FieldMathematics
TopicCOVID-19 epidemiological studies
Canadian institutionsConcordia University
Fundersnot available
KeywordsVaccinationSocial distanceCoronavirus disease 2019 (COVID-19)Public healthAsymptomaticMedicineDistancing2019-20 coronavirus outbreakVaccination policyEnvironmental healthVirologyDiseaseInfectious disease (medical specialty)OutbreakSurgery

Abstract

fetched live from OpenAlex

This study examines the success of COVID-19 vaccines in four European countries and Israel for the α variant. These countries respond to the vaccines with varying degrees of success. Countries with successful vaccination programs take about 160 days to get to the minimum number of new cases. Only Italy and Israel came close to eradicating the virus. Vaccines and previous infections have a similar prophylactic effect on new infections. Second doses for the most part add little protection to those who have only one dose. Vaccines become very effective after seven days although there are some added benefits that accrue to individuals in the second week after vaccination. The effect of vaccines on new cases is non-linear and exhibits a decreasing marginal effect. COVID-19 is spread by asymptomatic carriers, a feature of the disease which was discernable at the same time that public health agencies were discouraging the use of masks by the general public and downplaying the importance of social distancing. These were major policy errors and led to many unnecessary deaths.

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)0.005
metaresearch head score (Gemma)0.015
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.561
GPT teacher head0.531
Teacher spread0.030 · 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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