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Record W2315727141 · doi:10.2514/6.2012-2467

Cooperative Sensor Resource Management for Improved Situation Awareness

2012· article· en· W2315727141 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

VenueInfotech@Aerospace 2012 · 2012
Typearticle
Languageen
FieldEngineering
TopicMilitary Strategy and Technology
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsResource management (computing)Computer scienceResource (disambiguation)Wireless sensor networkKnowledge managementComputer network

Abstract

fetched live from OpenAlex

An approach to cooperative sensor management is proposed to coordinate track discovery, refinement, and maintenance that balances the workload across the available flight members, reduces the redundant data that is shared across the data link, and increases track capacity and system responsiveness to new objects in the environment. This method provides a distributed, but cooperative sensor management framework that allows optimization locally at each aircraft, but permits cooperation across a flight group (or larger community) autonomously to improve the operational picture of the flight group. The approach supports cooperative search schemes that focus different platforms on complementary subsets of the environment while still providing complete coverage as a group. Predictions for the expected improvement in flight group capacity and reductions in data link loading are shown to be proportional to the volume of the overlapping coverage.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.625
Threshold uncertainty score0.851

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 head0.013
GPT teacher head0.232
Teacher spread0.219 · 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