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Record W2955048699

Flipbook: visualização temporal de tumor cerebral

2016· dissertation· pt· W2955048699 on OpenAlexaboutno aff
Rodrigo Mansueli Nunes

Bibliographic record

Venuenot available
Typedissertation
Languagept
FieldSocial Sciences
TopicReflective Practices in Education
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPsychologyPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

De modo a atender a uma demanda especifica na area medica durante o estagio no Canada do programa Ciencia sem Fronteiras, criou-se o programa Flipbook para realizar a visualizacao cronologica de tumores cerebrais. Este e dividido entre um software que faz o processamento necessario para preparar as imagens no formato de arquivos DICOM usado na area medica e outro chamado de Viewer que faz as animacoes e interacoes entre as imagens para o usuario. Este trabalho tambem apresenta uma fundamentacao teorica basica para facilitar o entendimento do conteudo discursado no texto. Posteriormente, indica quais as ferramentas usadas para a realizacao do mesmo e a metodologia utilizada. Em seguida, discute detalhes especificos da implementacao do projeto. Por fim, faz uma conclusao sobre o desenvolvimento e indica as limitacoes do software e faz - se sugestoes para trabalhos futuros.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.031
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0310.003

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 head0.044
GPT teacher head0.450
Teacher spread0.405 · 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
Published2016
Admission routes1
Has abstractyes

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