MétaCan
Menu
Back to cohort
Record W2145733441 · doi:10.1147/sj.442.0289

The Eclipse 3.0 platform: Adopting OSGi technology

2005· article· en· W2145733441 on OpenAlexaff
Olivier Gruber, B. J. Hargrave, Jeff McAffer, Pascal Rapicault, Tucker Watson

Bibliographic record

VenueIBM Systems Journal · 2005
Typearticle
Languageen
FieldDecision Sciences
TopicScientific Computing and Data Management
Canadian institutionsIBM (Canada)
Fundersnot available
KeywordsEclipseOperating systemComputer sciencePlug-inSoftware engineeringEmbedded systemSystems engineeringEngineering

Abstract

fetched live from OpenAlex

From its inception Eclipse was mainly designed to be a tooling platform, but with Version 3.0, Eclipse is now evolving toward a Rich Client Platform (RCP). This change, driven by the open-source community, brought a whole set of new requirements and challenges for the Eclipse platform, such as dynamic plug-in management, services, security, and improved performance. This paper describes the path from the proprietary Eclipse 2.1 runtime to the new Eclipse 3.0 runtime based on OSGi® specifications. It details the motivation for such a change and discusses the challenges this change presented.

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)0.004
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0040.004
Open science0.0020.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.004

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 head0.145
GPT teacher head0.390
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

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 designNot applicable
Domainnot available
GenreEmpirical

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

Citations98
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueIBM Systems JournalSame topicScientific Computing and Data ManagementFrench-language works237,207