MétaCan
Menu
Back to cohort
Record W2071742378 · doi:10.4271/2013-01-2276

Defining Environmental Indicators at Detail Design Stage as Part of an Ecodesign Strategy

2013· article· en· W2071742378 on OpenAlexaff
Greg Doudrich, Guillaume Corriveau, Denis Walch, Kahina Oudjehani, Franck Dervault

Bibliographic record

VenueSAE International Journal of Aerospace · 2013
Typearticle
Languageen
FieldEngineering
TopicSustainable Industrial Ecology
Canadian institutionsBombardier (Canada)
Fundersnot available
KeywordsEcodesignStage (stratigraphy)EngineeringSystems engineeringManufacturing engineeringGeology

Abstract

fetched live from OpenAlex

<div class="section abstract"><div class="htmlview paragraph">Implementing Design for Environment (DfE) into the design process requires a strategic integration. Furthermore, as DfE is continuously evolving, flexible processes need to be implemented. This article focuses on the integration of DfE into an optimization framework with the objective of influencing next-generation aircraft. For this purpose, DfE and Structures groups are developing together a set of new environmental indicators covering all life cycle stages of the product by coupling a list of yes/no questions with an environmental matrix. The following indicators are calculated: Regulation risk, Impact of manufacturing the part, CO2 emissions and Recyclability potential. These indicators will be used as constraints in the multi-disciplinary design optimization (MDO) framework, meaning that the structure will be designed while complying with environmental targets and anticipating future regulation changes. To illustrate this new methodology, it is applied to three common processes used in structural design on a generic part: aluminum with chemical milling, aluminum with mechanical milling and composite produced by automated fiber placement process. Through this initiative, it is believed that in the mid-term, manufacturing processes selection will not only be influenced by technical and cost requirements, but also considering their environmental impacts.</div></div>

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.850
Threshold uncertainty score0.998

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.012
GPT teacher head0.231
Teacher spread0.219 · 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.

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

Citations2
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueSAE International Journal of AerospaceSame topicSustainable Industrial EcologyFrench-language works237,207