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A case study to estimate design effort for Pratt & Whitney canada

2008· article· en· W2106646002 on OpenAlex
Adil Salam, Nadia Bhuiyan, Gerard J. Gouw, Syed Asif Raza

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueInternational Journal of Management Science and Engineering Management · 2008
Typearticle
Languageen
FieldEngineering
TopicManufacturing Process and Optimization
Canadian institutionsUniversité de MontréalConcordia University
Fundersnot available
KeywordsParametric statisticsParametric modelComputer scienceEstimationGas compressorRotor (electric)Sensitivity (control systems)Industrial engineeringOperations researchEngineeringSystems engineeringMathematicsStatisticsMechanical engineering

Abstract

fetched live from OpenAlex

Abstract The design effort required to complete a project is an important aspect of a project. It impacts the final cost, as well as the lead-time of a project. In this paper, a case study, which is carried out at Pratt & Whitney Canada, a global leader in the design and manufacture of aircraft engines, is presented. A Parametric model is proposed to estimate the design effort required in for a particular department to complete their design phase of an integrated blade-rotor low-pressure compressor fan. In a sensitivity analysis, the model estimation is compared with the actual estimates and the comparison demonstrates that the parametric model results in a good estimation. The analysis further explores the impact of various factors used to develop the parametric model, as well as demonstrates the significance of the proposed modeling methodology.

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.

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)0.001
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: none
Teacher disagreement score0.536
Threshold uncertainty score0.499

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.019
GPT teacher head0.255
Teacher spread0.236 · 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