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Record W1860925290 · doi:10.1109/wwos.1989.109273

The Raven project (distributed object-oriented OS)

2003· article· en· W1860925290 on OpenAlexaff
Gerald Neufeld, Samuel T. Chanson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicDistributed and Parallel Computing Systems
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceObject (grammar)Context (archaeology)Distributed objectDatabase transactionFault toleranceObject-oriented programmingObject-relational mappingProgramming languageDistributed computingSoftware engineeringArtificial intelligenceMethod

Abstract

fetched live from OpenAlex

An overview is given of a distributed object-oriented operating system project called the Raven project. The Raven operating system is intended for use in a variety of applications, such as office systems and control systems. It is intended to work as a high-speed, fault-tolerant system. Raven includes both a programming language and an operating system. Raven is object-oriented in that it supports multiple inherited types. It is fault tolerant in that it supports persistent objects, recoverable objects, and atomic transactions. The major emphasis of the work is the overall design of the language and system, distributed configuration management, distributed single-level object store, RAM-based high-speed transaction management, and object-based parallel communication. All of these areas of work are being developed within the context of autonomous administration domains.>

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)0.003
metaresearch head score (Gemma)0.007
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: Software · Consensus signal: none
Teacher disagreement score0.042
Threshold uncertainty score0.139

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0050.007
Open science0.0030.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0420.056

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 head0.015
GPT teacher head0.249
Teacher spread0.234 · 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
GenreSoftware

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

Citations0
Published2003
Admission routes1
Has abstractyes

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