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Record W2108843826 · doi:10.1109/5289.887458

Sensor-based information appliances

2000· article· en· W2108843826 on OpenAlexafffund
V.Z. Groza, Dimitrios Makrakis, Dorina C. Petriu, N.D. Georganas, Emil M. Petriu

Bibliographic record

VenueIEEE Instrumentation & Measurement Magazine · 2000
Typearticle
Languageen
FieldComputer Science
TopicEnergy Efficient Wireless Sensor Networks
Canadian institutionsCarleton UniversityUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of TorontoUniversity of PatrasUniversity of Ottawa
KeywordsMultitudeUbiquitous computingEvent (particle physics)Wireless sensor networkComputer scienceComputer securityIntelligent sensorRisk analysis (engineering)BusinessHuman–computer interactionComputer network

Abstract

fetched live from OpenAlex

Using the existing home infrastructure based on open industry standards, we will be able to integrate the home network with external networks to easily manage home devices, both locally and remotely. The advent of pervasive computing marks an urgent need for a new generation of intelligent sensing agents and information appliances. It will also demand environments for resource management of broad applications involving loosely coupled, event-driven, diverse information appliances. It is suggested that the development of intelligent sensing agents and sensor-based information appliances will spread pervasive technology to a multitude of human activities such as mining and manufacturing, security, transportation, sports, and health care. The topics discussed also include management of heterogeneous functions and networking 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)0.000
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.022
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0020.003
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0220.016

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 head0.021
GPT teacher head0.224
Teacher spread0.203 · 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

Citations175
Published2000
Admission routes2
Has abstractyes

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