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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 OpenAlex

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.

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.

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 categoriesMeta-epidemiology (narrow), Scholarly communication
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 score0.571
Threshold uncertainty score1.000

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 communication0.0020.001
Open science0.0010.000
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 head0.059
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