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Nota Técnica da Sociedade Brasileira de Nutrição Oncológica sobre os Cuidados Nutricionais em Oncologia frente à Pandemia de Covid-19

2020· article· pt· W3040338588 on OpenAlexaff
Renata Brum Martucci, Ana Maria Cardoso, Carin Weirich Gallon, Erika Simone Coelho Carvalho, Izabella Fontenelle de Menezes Freitas, Lilianne Carvalho Santos Roriz, Luciana Zuolo Coppini, Luciane Beitler da Cruz, Maria Amélia Dantas, Maria Lúcia Varjão da Costa, Nádia Dias Gruezo, Viviane Dias Rodrigues, Nivaldo Barroso de Pinho

Bibliographic record

VenueRevista Brasileira de Cancerologia · 2020
Typearticle
Languagept
FieldComputer Science
TopicHealthcare during COVID-19 Pandemic
Canadian institutionsDiscovery Air (Canada)
FundersInstituto Nacional do Câncer, Ministério da SaúdeInstitut National Du Cancer
KeywordsMedicineCoronavirus disease 2019 (COVID-19)HumanitiesArtInternal medicine

Abstract

fetched live from OpenAlex

O objetivo da presente Norma Técnica é garantir as melhores condições de saúde dos pacientes e minimizar os riscos de infecção dos profissionais, pacientes e familiares.

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)0.010
metaresearch head score (Gemma)0.037
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.131
Threshold uncertainty score0.260

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.037
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.005
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.092
GPT teacher head0.363
Teacher spread0.271 · 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 designNot applicable
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
Published2020
Admission routes1
Has abstractyes

Explore more

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