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Record W1562490320 · doi:10.1109/vl.1996.545301

A visual language for the design of structured graphical objects

2002· article· en· W1562490320 on OpenAlexaff
P.T. Cox, Trevor J. Smedley

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicLogic, programming, and type systems
Canadian institutionsTechnical University of Nova Scotia
Fundersnot available
KeywordsComputer scienceProgramming languageVHDLDebuggingHardware description languageArtifact (error)Very-large-scale integrationVisual programming languageDesign languageComputer hardwareArtificial intelligenceEmbedded systemField-programmable gate array

Abstract

fetched live from OpenAlex

The design of abstract or physical structures has much in common with the design of software structures, particularly when the structure in question has a mechanical or computational behaviour; such as a digital circuit. Like programming language systems, design systems must have expressive power sufficient for representing any design, a simulation mechanism for debugging the artifact under construction, and a production mechanism; for example, compilation for a programming language, or chip fabrication for a VLSI design system. Since specifying complex devices requires repetitive and conditional structures analogous to iteration, recursion and conditionals in programs, languages for designing complex devices are usually based on textual programming languages, for example VHDL for VLSI design. The advent of full featured visual programming languages, however raises the possibility that the mechanisms used to visually express compact and powerful program structures could be generalised to design languages. We consider using these mechanisms to express the design of structured graphical objects.

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.

How this classification was reachedexpand

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.986
Threshold uncertainty score0.145

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 head0.031
GPT teacher head0.266
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2002
Admission routes1
Has abstractyes

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