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SOCIAL ISOLATION MEASURES CAUSE REDUCTION IN THE CONTAMINATION AND DEATHS BY COVID-19? THE CASE OF THE MUNICIPALITY OF ARARAQUARA, SP, BRAZIL

2021· article· pt· W3203943421 on OpenAlex
Bruno Santos Francisco, Felipe Bueno Dutra, Abner Carvalho‐Batista, Fernanda Gordo Peres Francisco, Milena Sciacio Ghidini, Ricardo de Souza Cardoso, Gabriel Perussi, Lausanne Soraya de Almeida, José Mauro Santana da Silva, Fátima Conceição Márquez Piña-Rodrigues

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInterfaces Científicas - Saúde e Ambiente · 2021
Typearticle
Languagept
FieldMathematics
TopicCOVID-19 epidemiological studies
Canadian institutionsAdidas (Canada)
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Political scienceMedicineDisease

Abstract

fetched live from OpenAlex

A pandemia SARS-Cov-2 estabeleceu a necessidade de adoção de medidas restritivas para conter a disseminação do vírus. Em 2021, devido ao elevado número de casos de COVID-19 no município de Araraquara, Brasil, e após o esgotamento das vagas hospitalares em 2021, foi anunciado um bloqueio. Analisamos o efeito do distanciamento social dessa cidade, utilizando dados fornecidos pela prefeitura municipal ao longo de um período total de 90 dias. Usamos esses dados em uma tabela de vida, uma importante ferramenta que avalia o impacto de doenças na dinâmica populacional de uma espécie. Os resultados indicaram uma taxa básica de mortalidade de 0,0138 no período analisado e uma redução considerável no número de casos infectados e óbitos por COVID-19 após 24 dias de isolamento. Nossos resultados mostraram a eficácia do distanciamento social em conter a propagação da doença, com redução de 80% no número de óbitos, bem como a utilidade da tábua de vida como ferramenta útil para análise de dados.

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.

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)0.005
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.161
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.432
Teacher spread0.242 · 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