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Record W2164913620 · doi:10.3233/978-1-60750-065-0-7

The SCOOP Concurrency Model in Java-like Languages

2009· book-chapter· en· W2164913620 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

VenueIOS Press eBooks · 2009
Typebook-chapter
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSCOOPProgramming languageJavaComputer scienceConcurrency

Abstract

fetched live from OpenAlex

SCOOP is a minimal extension to the sequential object-oriented programming model for concurrency. The extension consists of one keyword (separate) that avoids explicit thread declarations, synchronized blocks, explicit waits, and eliminates data races and atomicity violations by construction, through a set of compiler rules. SCOOP was originally described for the Eiffel programming language. This paper makes two contributions. Firstly, it presents a design pattern for SCOOP, which makes it feasible to transfer SCOOP's concepts to different object-oriented programming languages. Secondly, it demonstrates the generality of the SCOOP model by presenting an implementation of the SCOOP design pattern for Java. Additionally, we describe tools that support the SCOOP design pattern, and give a concrete example of its use in Java.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.701
Threshold uncertainty score1.000

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 science0.0020.000
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 head0.033
GPT teacher head0.277
Teacher spread0.245 · 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