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Record W4379520388 · doi:10.55905/revconv.16n.6-020

A influência da qualidade habitacional na pandemia de Covid-19 nas cidades do Porto e Lisboa

2023· article· pt· W4379520388 on OpenAlexaff
Luiz Antônio Perrone Ferreira de Brito, Flavio Brant Mourão, Juliana da Camara Abitante, Tiana Clarisse Menezes Darwich

Bibliographic record

VenueContribuciones a las Ciencias Sociales · 2023
Typearticle
Languagept
FieldEnvironmental Science
TopicCOVID-19 impact on air quality
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)HumanitiesPolitical sciencePhilosophyMedicineInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

A necessidade do distanciamento social e do confinamento compulsório devido à pandemia da doença respiratória Covid-19 aconteceu em todo o mundo, incluindo Portugal. A pandemia também trouxe à tona a discussão sobre o papel das habitações na disseminação de doenças. Porto e Lisboa são as duas maiores cidades de Portugal, as mais adensadas e estão nas regiões que apresentaram o maior número de casos de Covid-19. O objetivo desta pesquisa é estudar a influência das edificações na contaminação e letalidade do Covid-19 nas regiões do Porto e Lisboa. Desta forma contribuiu para que melhores decisões de saúde pública e de legislação habitacional possam ser tomadas no combate e prevenção de doenças respiratórias em geral. Foi possível estabelecer uma relação entre a qualidade das habitações e a propagação da doença no país, ou seja, as edificações contribuem diretamente para o contágio da doença no que tange a aglomeração, ventilação e exposição solar.

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)0.001
metaresearch head score (Gemma)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.156
Threshold uncertainty score0.310

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0020.002
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.120
GPT teacher head0.390
Teacher spread0.270 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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