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Record W4394834800 · doi:10.4322/rae.v12n2.e201716.en

SIMULATION SUPPORTS AS INTERMEDIATE DESIGN OBJECTS: EXPERIENCES IN THE PETROLEUM REFINING INDUSTRY

2017· article· en· W4394834800 on OpenAlexaff
Daniel Braatz, Nilton Luiz Menegon

Bibliographic record

VenueRevista Ação Ergonômica · 2017
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsRefining (metallurgy)Oil refineryPetroleum industryPetroleum engineeringPetroleumComputer scienceProcess engineeringManufacturing engineeringBiochemical engineeringEngineeringChemistryWaste managementMetallurgyMaterials scienceEnvironmental engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

A ergonomia se preocupa em compreender o trabalho para transformá-lo. Para aumentar sua capacidade de intervenção efetiva esta disciplina se aproxima da engenharia, em especial da engenharia de produção, buscando métodos, técnicas e ferramentas que a auxiliem no processo de concepção de situações produtivas. As áreas do conhecimento relacionadas ao design de engenharia e, em especial, do projeto do trabalho, podem colaborar substancialmente para a efetividade da incorporação da perspectiva da atividade (segundo conceito da ergonomia situada) neste processo. A partir de uma articulação teórica e conceitual, que serviu como referencial da pesquisa de campo em uma indústria de refino de petróleo, busca-se compreender como diferentes suportes de simulação foram determinantes para a incorporação das racionalidades, interesses, restrições e expectativas dos atores participantes do processo de concepção. A pesquisa apresenta recomendações para que o projeto de situações produtivas comporte uma concepção continuada e distribuída, tendo a simulação como instrumento orientado ao objeto (ação projetual do sistema técnico), ao outro (ação coordenada) e ao próprio sujeito (ao comportar espaço para seu desenvolvimento, aprendizado e transformação).

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.382
Threshold uncertainty score0.441

Codex and Gemma teacher scores by category

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

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 head0.026
GPT teacher head0.277
Teacher spread0.250 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2017
Admission routes1
Has abstractyes

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