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Record W1970916397 · doi:10.1115/detc2005-84325

A VR-Based Data Retrieval System

2005· article· en· W1970916397 on OpenAlexaff
Chunsheng Yu, Qingjin Peng, Lushen Wu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicManufacturing Process and Optimization
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsComputer scienceProduct (mathematics)Product designConcurrent engineeringProduct engineeringProcess (computing)New product developmentProduct data managementVirtual realityProduct lifecycleSystems engineeringInformation retrievalHuman–computer interactionDatabaseManufacturing engineeringEngineeringProcess integrationProcess engineering

Abstract

fetched live from OpenAlex

Concurrent Engineering (CE) considers a product design and manufacturing simultaneously. The CE research has led to the product information integration. However, the current CE research focuses on smooth of the product information flow rather than a user-friendly searching process for users to obtain product information quickly. A new data retrieval system for product design and manufacturing is proposed in this paper. The system not only provides an integrated product database, but also uses Virtual Reality (VR) and Web technologies for easing navigation in product data and for accelerating access to useful information. The effective collaboration in a product design and manufacturing can be achieved through the proposed information searching method. Examples are presented in the discussion.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0170.009

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 head0.020
GPT teacher head0.217
Teacher spread0.197 · 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 designBench or experimental
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
Published2005
Admission routes1
Has abstractyes

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