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Record W3029095612 · doi:10.7326/m20-2945

Risk for COVID-19 Resurgence Related to Duration and Effectiveness of Physical Distancing in Ontario, Canada

2020· article· en· W3029095612 on OpenAlexafffundabout
Ashleigh R. Tuite, Amy L. Greer, Steven De Keninck, David N. Fisman

Bibliographic record

VenueAnnals of Internal Medicine · 2020
Typearticle
Languageen
FieldMathematics
TopicCOVID-19 epidemiological studies
Canadian institutionsUniversity of GuelphPublic Health OntarioUniversity of Toronto
FundersInstitute of Infection and ImmunityCanadian Institutes of Health Research
KeywordsPandemicCoronavirus disease 2019 (COVID-19)MedicineDistancingPsychological interventionSocial distance2019-20 coronavirus outbreakTransmission (telecommunications)Environmental healthSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)PopulationGlobeDuration (music)DemographyGerontologyDiseaseVirologyOutbreakInfectious disease (medical specialty)Nursing

Abstract

fetched live from OpenAlex

Background: Insights from epidemiologic models have helped to guide and improve understanding of mitigation policies for coronavirus disease 2019 (COVID-19) across the globe.As the pandemic progresses, models can be used to quantify what may unfold when such measures are relaxed.Objective: To explore the effect of physical distancing measures on COVID-19 transmission in the population of Ontario, Canada.Methods and Findings: We previously described a transmission model of COVID-19, stratified by age and health status, in the Canadian province of Ontario (1).It evaluated nonpharmaceutical interventions to control the COVID-19

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 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)0.063
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.135
Threshold uncertainty score0.945

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.063
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.190
GPT teacher head0.441
Teacher spread0.251 · 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 teacher head, not a consensus.

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

Citations31
Published2020
Admission routes3
Has abstractyes

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