MétaCan
Menu
Back to cohort
Record W2166745412 · doi:10.1109/imtc.2005.1604178

A Web-Services Framework for 1451 Sensor Networks

2006· article· en· W2166745412 on OpenAlexaff
E.F. Sadok, Ramiro Liscano

Bibliographic record

Venue2005 IEEE Instrumentationand Measurement Technology Conference Proceedings · 2006
Typearticle
Languageen
FieldComputer Science
TopicSensor Technology and Measurement Systems
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsComputer scienceWeb serviceSOAPWS-PolicyDevices Profile for Web ServicesXMLWS-AddressingWS-I Basic ProfileWorld Wide WebWeb modelingDatabaseWeb mappingWeb application securityWeb development

Abstract

fetched live from OpenAlex

The following paper provides an architectural proposal and integrate common Web-services methodologies (XML based messaging, XML based transformation tools, Web-service tools and platforms) in order to facilitate the development of a Web-services based 1451.1 NCAP information model. A distinct separation exists within the 1451.1 NCAP model based on an internal service structure (characterized as internal methods/functions for representation of the 1451.1 object-oriented models) and an external service structure (characterized as 1451 standards based interfaces to transducer components and external network communications). Network visible operations, such as publishing sensory data-readings, are facilitated via a Web-services communication platform, based on WSDL interfaces and WS-eventing services bound to the SOAP protocol. This work entails in the development of a software/Web-services based tool which can expose internal NCAP function-block operations/methods as network visible operations to a remote HMI. The NCAP's data-model is modelled via an XML data representation which complies with the integrity of the proposed IEEE 1451.1 standard's object-oriented model

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)0.004
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.003
Science and technology studies0.0020.002
Scholarly communication0.0060.004
Open science0.0040.004
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0070.008

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 head0.029
GPT teacher head0.246
Teacher spread0.218 · 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 designNot applicable
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

Citations24
Published2006
Admission routes1
Has abstractyes

Explore more

Same venue2005 IEEE Instrumentationand Measurement Technology Conference ProceedingsSame topicSensor Technology and Measurement SystemsFrench-language works237,207