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Record W4378348687 · doi:10.3389/fpubh.2023.1173673

Counterfactuals of effects of vaccination and public health measures on COVID-19 cases in Canada: what could have happened?

2023· article· en· W4378348687 on OpenAlexaffabout
David Vickers, Stefan Eberspaecher, Claudia Chaufan, Steven Pelech

Bibliographic record

VenueFrontiers in Public Health · 2023
Typearticle
Languageen
FieldMathematics
TopicCOVID-19 epidemiological studies
Canadian institutionsKinexus Bioinformatics Corporation (Canada)University of British ColumbiaYork UniversityUniversity of Calgary
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)VaccinationCounterfactual conditional2019-20 coronavirus outbreakPublic healthSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)MedicinePandemicVirologyPsychologyOutbreakNursingSocial psychologyCounterfactual thinkingDiseaseInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

OPINION article Front. Public Health, 09 May 2023Sec. Infectious Diseases: Epidemiology and Prevention Volume 11 - 2023 | https://doi.org/10.3389/fpubh.2023.1173673

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)0.065
metaresearch head score (Gemma)0.153
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.169
Threshold uncertainty score0.343

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0650.153
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0020.003
Science and technology studies0.0020.006
Scholarly communication0.0060.003
Open science0.0040.003
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0190.001

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 head0.306
GPT teacher head0.427
Teacher spread0.121 · 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 designSimulation or modeling
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
Published2023
Admission routes2
Has abstractyes

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