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Record W4319313779 · doi:10.5151/ped2022-5134714

Design e Materiais no desenvolvimento de Produtos: uma experiência didática

2022· article· pt· W4319313779 on OpenAlexaff
Douglas Daniel PEREIRA, Jamille Noretza de Lima Pereira LANUTTI

Bibliographic record

Venuenot available
Typearticle
Languagept
FieldSocial Sciences
TopicChemistry Education and Research
Canadian institutionsAdidas (Canada)
Fundersnot available
KeywordsHumanitiesComputer sciencePhilosophy

Abstract

fetched live from OpenAlex

Produtos desenvolvidos são compostos pelos mais variados materiais, que dão forma, compõem características e influenciam a interação com os artefatos. Levando em conta a importância de compreender e aplicar e selecionar o melhor material durante as etapas de desenvolvimento de produtos em Design, este trabalho tem o objetivo de apresentar uma atividade didática desenvolvida no curso de Design, na disciplina ‘Princípios e aplicações de materiais e processos’, demonstrando algumas das etapas e os resultados obtidos a partir da experiência didática. Tratam-se de Fichas produzidas pelos alunos por meio de pesquisa, permitindo que conheçam os diferentes tipos de materiais, suas características e aplicações, que podem ser utilizadas durante o desenvolvimento do projeto com o intuito de auxiliar na seleção mais adequada para produção de um produto final.

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)0.009
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.004
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.087
GPT teacher head0.387
Teacher spread0.301 · 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 designQualitative
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

Citations0
Published2022
Admission routes1
Has abstractyes

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