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Record W2981749759 · doi:10.1007/s00165-019-00493-0

Multiple model synchronization with multiary delta lenses with amendment andK-Putput

2019· article· en· W2981749759 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

VenueFormal Aspects of Computing · 2019
Typearticle
Languageen
FieldComputer Science
TopicModel-Driven Software Engineering Techniques
Canadian institutionsMcMaster University
Fundersnot available
KeywordsComputer scienceCompatibility (geochemistry)Binary numberSynchronization (alternating current)Consistency (knowledge bases)Lens (geology)Theory of computationComposition (language)UnderpinningTheoretical computer scienceThrough-the-lens meteringAlgorithmLawMathematicsArtificial intelligenceTelecommunicationsPhysicsArithmeticOpticsPolitical scienceEngineering

Abstract

fetched live from OpenAlex

Abstract Multiple (more than 2) model synchronization is ubiquitous and important for MDE, but its theoretical underpinning gained much less attention than the binary case. Specifically, the latter was extensively studied by the bx community in the framework of algebraic models for update propagation called lenses . We make a step to restore the balance and propose a notion of multiary delta lens. Besides multiarity, our lenses feature reflective updates, when consistency restoration requires some amendment of the update that violated consistency, and a reasonable Put Put law that requires compatibility of update propagation with update composition for a precisely specified restricted class of composable update pairs. We emphasize the importance of various ways of lens composition for practical applications of the framework, and prove several composition results.

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 score0.269
Threshold uncertainty score0.776

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 communication0.0000.001
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 head0.006
GPT teacher head0.197
Teacher spread0.191 · 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