MétaCan
Menu
Back to cohort
Record W1977888290 · doi:10.1145/2432497.2432499

Towards a model transformation intent catalog

2012· article· en· W1977888290 on OpenAlexaff
Moussa Amrani, Jürgen Dingel, Leen Lambers, Lucio Levi, Rick Salay, Gehan Selim, Eugene Syriani, Manuel Wimmer

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicModel-Driven Software Engineering Techniques
Canadian institutionsUniversity of TorontoMcGill UniversityQueen's University
Fundersnot available
KeywordsComputer scienceModel transformationTransformation (genetics)Model-driven architectureSoftware engineeringSoftwareProgramming languageInformation retrievalSoftware developmentArtificial intelligence

Abstract

fetched live from OpenAlex

We report on our ongoing effort to build a catalog of model transformation intents that describes common uses of model transformations in Model-Driven Engineering (MDE) and the properties they must or may possess. We present a preliminary list of intents and common properties. One intent (transformation for analysis) is described in more detail and the description is used to identify transformations with the same intent in a case study on the use of MDE techniques for the development of control software for a power window.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.030
metaresearch head score (Gemma)0.039
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods
Teacher disagreement score0.030
Threshold uncertainty score0.159

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.039
Meta-epidemiology (narrow)0.0030.004
Meta-epidemiology (broad)0.0030.005
Bibliometrics0.0240.015
Science and technology studies0.0030.002
Scholarly communication0.0110.021
Open science0.0040.007
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0070.005

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 head0.025
GPT teacher head0.249
Teacher spread0.224 · 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 source (direct Gemma or distilled Codex), 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

Citations33
Published2012
Admission routes1
Has abstractyes

Explore more

Same topicModel-Driven Software Engineering TechniquesFrench-language works237,207