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Web Service Enabled Online Laboratory

2013· book-chapter· en· W4248211416 on OpenAlexaff
Yuhong Yan, Yong Liang, Abhijeet Roy, Xinge Du

Bibliographic record

VenueIGI Global eBooks · 2013
Typebook-chapter
Languageen
FieldComputer Science
TopicSoftware System Performance and Reliability
Canadian institutionsNational Research Council CanadaUniversity of New BrunswickConcordia University
Fundersnot available
KeywordsWeb serviceComputer scienceWeb applicationService-oriented architectureUsabilityWorld Wide WebInteroperabilitySOAPWS-PolicyService (business)Web modelingSoftware portabilityRich Internet applicationWeb developmentSoftware engineeringWeb application securityHuman–computer interactionOperating system

Abstract

fetched live from OpenAlex

Online experimentation allows students from anywhere to operate remote instruments at any time. The current techniques constrain users to bind to products from one company and install client side software. We use Web services and Service Oriented Architecture to improve the interoperability and usability of the remote instruments. Under a service oriented architecture for online experiment system, a generic methodology to wrap commercial instruments using IVI and VISA standard as Web services is developed. We enhance the instrument Web services into stateful services so that they can manage user booking and persist experiment results. We also benchmark the performance of this system when SOAP is used as the wire format for communication and propose solutions to optimize performance. In order to avoid any installation at the client side, the authors develop Web 2.0 based techniques to display the virtual instrument panel and real time signals with just a standard Web browser. The technique developed in this article can be widely used for different real laboratories, such as microelectronics, chemical engineering, polymer crystallization, structural engineering, and signal processing.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.782
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.003

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 head0.228
Teacher spread0.216 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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
Published2013
Admission routes1
Has abstractyes

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