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Record W1512587630 · doi:10.1109/mim.2004.1337913

Robotic sensor agents

2004· article· en· W1512587630 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueIEEE Instrumentation & Measurement Magazine · 2004
Typearticle
Languageen
FieldComputer Science
TopicRobotic Path Planning Algorithms
Canadian institutionsInformation Technology Association of CanadaCarleton UniversityUniversity of Ottawa
Fundersnot available
KeywordsHomeland securityEnvironmental monitoringWireless sensor networkEmbedded systemHazardous wasteComputer scienceField (mathematics)Task (project management)EngineeringReal-time computingSystems engineeringOperating systemEnvironmental engineeringWaste managementGeography

Abstract

fetched live from OpenAlex

Monitoring environment parameters is a complex task of great importance in many areas, such as the natural living environment; homeland security; industrial or laboratory hazardous environments (biologically, radioactively, or chemically contaminated); polluted/toxic natural environments; water treatment plants; nuclear stations; war zones; or remote, difficult-to-reach environments, such as the deep space or underwater. This article will discuss a new generation of intelligent, autonomous, wireless robotic sensor agents (RSAs) for complex environment monitoring. Shown in this article is the architecture of an RSA system under development in our laboratory at the University of Ottawa (see Petriu et al., p14-19, May 2002). Monitoring is done by continuously collecting sensory data from stationary and mobile RSAs deployed in the field.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.701
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.063
GPT teacher head0.276
Teacher spread0.213 · 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