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Record W2112292901 · doi:10.1109/iceccs.1996.558401

Understanding large-scale behaviour patterns in complex systems

2002· article· en· W2112292901 on OpenAlex
R. J. A. Buhr

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
TopicAdvanced Software Engineering Methodologies
Canadian institutionsCarleton University
Fundersnot available
KeywordsComputer scienceComplex systemSoftware systemSoftwareClass (philosophy)Class diagramInheritance (genetic algorithm)Scale (ratio)Code (set theory)Theoretical computer scienceSoftware engineeringProgramming languageArtificial intelligenceData scienceHuman–computer interactionUnified Modeling LanguageSet (abstract data type)

Abstract

fetched live from OpenAlex

Understanding how a complex system works as a whole can be difficult because it requires blending information about structure and behaviour into a coherent whole that can be understood without reference to details of how its parts are constructed, behave internally, or interact. The problem is doubly difficult for software systems, because we do not know any good large-scale models of such systems to keep in the mind's eye. We have details in code files, low-level diagrams of software details (for example, class inheritance hierarchies), and system views of hardware environments, but these are not enough. We suggest that model of whole systems that we can diagram and hold in the mind's eye are so important for human understanding of complex systems of all kinds that, if they do not exist, they must be invented. Use case maps are an example of a model invented for this purpose. While use case maps were invented to deal with the problems of understanding software systems, they are useful for complex systems of all kinds.

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: Methods · Consensus signal: none
Teacher disagreement score0.734
Threshold uncertainty score0.431

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 head0.285
GPT teacher head0.309
Teacher spread0.024 · 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