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Record W2094844955 · doi:10.1002/sys.20133

Human view dynamics—The NATO approach

2009· article· en· W2094844955 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueSystems Engineering · 2009
Typearticle
Languageen
FieldComputer Science
TopicPetri Nets in System Modeling
Canadian institutionsnot available
Fundersnot available
KeywordsInteroperabilityComputer scienceSystems engineeringSystem dynamicsModeling and simulationSoftware engineeringData scienceEngineeringSimulationArtificial intelligenceWorld Wide Web

Abstract

fetched live from OpenAlex

Abstract A methodology was established to use the data captured in the NATO Human View to populate a simulation model; this provides a dynamic instantiation of the Human View data. The Improved Performance Research Integration Tool (IMPRINT), provided by the U.S. Army Research Laboratory was used as the simulation environment. By creating the Human View Dynamics, the data captured in the static products can be evaluated through tradeoff analysis and performance criteria. The demonstrated interoperability of the Human View static products and the IMPRINT dynamic modeling capability resulted in a mapping between the two domains. Additionally, the capability of the Integrated Performance Modeling Environment (IPME) used by Canada and the United Kingdom as a simulation environment was reviewed. © 2009 Wiley Periodicals, Inc. Syst Eng

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 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 score0.987
Threshold uncertainty score0.613

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 communication0.0000.000
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 head0.015
GPT teacher head0.222
Teacher spread0.207 · 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