MétaCan
Menu
Back to cohort
Record W1857976572 · doi:10.1109/ific.2000.862654

An architecture and a facility for the integration of all levels of data fusion

2000· article· en· W1857976572 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicTarget Tracking and Data Fusion in Sensor Networks
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsEnablingSituation awarenessBaseline (sea)Computer scienceKey (lock)Situation analysisArchitectureSensor fusionProcess (computing)Bootstrapping (finance)Systems engineeringData integrationEngineeringArtificial intelligenceComputer securityData miningOperating system

Abstract

fetched live from OpenAlex

Data fusion is a key enabler for the situation analysis process. The study and the integration of all data fusion levels require both a suited architecture, and a modeling and simulation environment that allows the technological demonstration of the situation analysis concepts to be investigated, and also the demonstration of their supportive contribution to the situational awareness state. This paper discusses both of these aspects. In particular, it focuses on design issues related to development of a simulation environment for the analysis of the tactical situation. Finally, the paper describes a bootstrapping effort to develop a baseline of this environment. This baseline has been successfully used to support the analysis of threat evaluation algorithms for the command and control system of the IROQUOIS class ships.

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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.994
Threshold uncertainty score0.146

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 science0.0010.000
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 head0.093
GPT teacher head0.321
Teacher spread0.228 · 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