MétaCan
Menu
Back to cohort
Record W1977415233 · doi:10.1177/003754970107600605

A Simulation Environment for Multi-Sensor Planning

2001· article· en· W1977415233 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.

Bibliographic record

VenueSIMULATION · 2001
Typearticle
Languageen
FieldEngineering
TopicRobotics and Sensor-Based Localization
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSoftware deploymentFlexibility (engineering)Computer scienceTask (project management)Real-time computingArchitectureWireless sensor networkHuman–computer interactionEmbedded systemArtificial intelligenceSystems engineeringEngineeringSoftware engineeringComputer network

Abstract

fetched live from OpenAlex

The aim of this work is to design a flexible multi- agent environment for simulating the deployment of multiple sensors for a given sensing task. The envi ronment is based on a multi-agent architecture such that each sensor is controlled by an autonomous in telligent agent. This allows the user great flexibility in manipulating the number of sensors and also the capabilities of each sensor. The simulation system permits the user to visualize the outcome of the de ployment for various sensor configurations prior to implementation in the real world. Currently, the system focuses on the use of cameras for visual in spection tasks in which a single camera would be inadequate due to occluding objects in the scene or the sheer size of the target.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.882
Threshold uncertainty score0.465

Codex and Gemma teacher scores by category

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

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 head0.059
GPT teacher head0.294
Teacher spread0.235 · 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