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Uma entrevista com José Thiers Carneiro Júnior

2020· article· pt· W3106376992 on OpenAlexaboutno aff
Jonathan Ribeiro

Bibliographic record

VenueJournal of the Brazilian College of Oral and Maxillofacial Surgery · 2020
Typearticle
Languagept
FieldSocial Sciences
TopicScience and Science Education
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineOral surgeryHumanitiesGeneral surgeryDentistryArt

Abstract

fetched live from OpenAlex

Conselheiro e Membro Titular do Colégio Brasileiro de Cirurgia e Traumatologia Buco-Maxilo-Facial. » Docente do Programa de Pós-Graduação em Odontologia da Universidade Federal do Pará (UFPA). » Coordenador do programa de Residência em Cirurgia e Traumatologia Buco-Maxilo-Facial do Hospital Ophir Loyola. » Professor do Curso de Especialização em Cirurgia e Traumatologia Buco-Maxilo-Facial da Associação Brasileira de Odontologia - Seção Pará. » Fellow of International Association of Oral & Maxillofacial Surgery. » Doutor em Neurociências e Biologia Celular pela UFPA. » Mestre em Ciências Morfofuncionais pela Universidade de São Paulo. » Especialista em Cirurgia e Traumatologia Bucomaxilofacial (SCM/SP - CBCTBMF). » Oral & Maxillofacial Surgery Fellowship - Dalhousie University, Canadá.

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)0.006
metaresearch head score (Gemma)0.029
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.029
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0030.002
Scholarly communication0.0060.005
Open science0.0020.003
Research integrity0.0050.009
Insufficient payload (model declined to judge)0.0230.008

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 head0.057
GPT teacher head0.296
Teacher spread0.240 · 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
GenreOther

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
Published2020
Admission routes1
Has abstractyes

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