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Record W2620154287 · doi:10.1109/syscon.2017.7934715

A next generation collaborative system for micro devices assembly

2017· article· en· W2620154287 on OpenAlexaff
J. Cecil, Raviteja Gunda, Aaron Cecil-Xavier

Bibliographic record

Venue2017 Annual IEEE International Systems Conference (SysCon) · 2017
Typearticle
Languageen
FieldEngineering
TopicManufacturing Process and Optimization
Canadian institutionsStillwater (Canada)
FundersLos Alamos National LaboratoryVirginia Agricultural Experiment Station, Virginia Polytechnic Institute and State UniversityMozilla Foundation
KeywordsThe InternetComputer scienceVirtual realityNext-generation networkInterface (matter)Plan (archaeology)Collaborative softwareHuman–computer interactionOperating system

Abstract

fetched live from OpenAlex

This paper discusses the development of an advanced collaborative system to support the assembly of micro devices. The overall system Virtual Reality based assembly analysis environment (VAE) which is part of a larger collaborative framework for the emerging domain of Micro Devices Assembly (MDA). MDA involves the assembly of micron sized devices which cannot be manufactured by Micro electro mechanical systems (MEMS) technologies. The VAE is comprised of several modules including an assembly plan generator, path planner and a network based cyber physical interface which allows it to support collaboration involving distributed users. As the current Internet has several limitations, a major initiative is underway to develop the Next Generation Internet frameworks which can reduce latency, increase the bandwidth of data exchange and support distributed collaboration. VAE has been implemented as part of a national initiative aimed at exploring Next Generation Internet technologies.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.017
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0020.002
Open science0.0020.004
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0170.004

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 head0.059
GPT teacher head0.293
Teacher spread0.234 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations4
Published2017
Admission routes1
Has abstractyes

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