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Record W4404790395 · doi:10.15628/rbept.2024.15165

Quimare Soluções LTDA

2024· article· pt· W4404790395 on OpenAlexaff
Nayana Bruna Nery Monção, José Ribamar Lopes Batista Júnior

Bibliographic record

VenueRevista Brasileira da Educação Profissional e Tecnológica · 2024
Typearticle
Languagept
FieldEnvironmental Science
TopicEnvironmental Sustainability and Education
Canadian institutionsCanadian Turfgrass Research Foundation
Fundersnot available
KeywordsBusiness

Abstract

fetched live from OpenAlex

O presente trabalho apresenta dados da aplicação de uma WebQuest, que foi norteada considerando a rotina de uma empresa fictícia - QUIMARE Soluções LTDA. Por meio dela, estimulou-se estudantes do Ensino Médio a desenvolverem produtos de higiene e de cuidado pessoal. Para tal, organizou-se uma ação contendo oito trilhas, onde cada uma delas solicitava a execução de uma tarefa e, de forma colaborativa, as turmas organizavam-se para apresentar os resultados de acordo com o que era solicitado. Os dados produzidos tiveram perfil multimodal, pois vários recursos puderam ser explorados. Neste processo, reforça-se sobre a potencialidade do uso dessa metodologia, pois, neste caso, os estudantes puderam atuar como protagonistas no desenvolvimento de seus conhecimentos de várias áreas do saber.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.962
Threshold uncertainty score0.127

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0030.001
Scholarly communication0.0040.003
Open science0.0010.003
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0380.010

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 head0.024
GPT teacher head0.303
Teacher spread0.279 · 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.

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

Citations1
Published2024
Admission routes1
Has abstractyes

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