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Record W2108843826 · doi:10.1109/5289.887458

Sensor-based information appliances

2000· article· en· W2108843826 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.
fundA Canadian funder is recorded on the work.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.791
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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