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Record W2887942074 · doi:10.5935/abc.20180154

1º Posicionamento Brasileiro sobre o Impacto dos Distúrbios de Sono nas Doenças Cardiovasculares da Sociedade Brasileira de Cardiologia

2018· article· pt· W2887942074 on OpenAlexaff
Luciano F. Drager, Geraldo Lorenzi‐Filho, Fátima Dumas Cintra, Rodrigo Pinto Pedrosa, Lia Bittencourt, Dalva Poyares, Rogério Santos‐Silva, Pedro Felipe Carvalhedo de Bruin, Glaucylara Reis Geovanini, Felipe N. Albuquerque, Wercules Oliveira, Gustavo Antônio Moreira, Linda Massako Ueno, Flávia Baggio Nerbass, Maria Urbana Pinto Brandão Rondon, Eline Rozária Ferreira Barbosa, Adriana Bertolami, Ângelo Amato Vincenzo de Paola, Betânia Braga Silva Marques, Camila Futado Rizzi, Carlos Eduardo Negrão, Carlos Henrique Gomes Uchôa, Cristiane Maki‐Nunes, Dênis Martinez, Edmundo Arteaga Fernández, Fabrizio Urbinati Maroja, Fernanda R. Almeida, Ivani C. Trombetta, Luciana Julio Storti, Luiz Aparecido Bortolotto, Marco Túlio de Mello, Melania Aparecida Borges, Mônica L. Andersen, Natanael de Paula Portilho, Paula Macedo, Rosana Alves, Sérgio Tufik, Simone Chaves Fagondes, Thaís Telles Risso

Bibliographic record

VenueArquivos Brasileiros de Cardiologia · 2018
Typearticle
Languagept
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineHumanities

Abstract

fetched live from OpenAlex

Americanae nace como un proyecto conjunto que surge dentro de la Red Europea de Información y Documentación sobre América Latina (REDIAL), y que ha afrontado la Biblioteca de la Agencia Española de Cooperación Internacional para el Desarrollo (AECID). Esta nueva biblioteca virtual hace más accesibles los libros digitales de tema americanista a los investigadores y usuarios interesados de cualquier parte del mundo.

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)0.009
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.282
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.005
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0010.002
Science and technology studies0.0020.003
Scholarly communication0.0010.001
Open science0.0030.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0010.002

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 head0.038
GPT teacher head0.313
Teacher spread0.274 · 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; both teacher heads agree on what is shown here.

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

Citations33
Published2018
Admission routes1
Has abstractyes

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