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Record W3145824319 · doi:10.1109/dsd.2007.4341465

An Embedded Implementation of the Microsoft Common Language Infrastructure

2007· article· en· W3145824319 on OpenAlexaff
Joey C. Libby, Kenneth B. Kent

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsComputer scienceAbstractionAbstraction layerLayer (electronics)ArchitectureSet (abstract data type)Instruction setEmbedded systemOperating systemSoftware engineeringComputer architectureSoftwareProgramming language

Abstract

fetched live from OpenAlex

The common language infrastructure (CLI) provides a framework for managing and executing applications. Developers designing applications for the CLI need not worry about the underlying architecture as it is abstracted from view by the CLI framework. This abstraction, while a boon for developers, leads to degraded performance. It is because of these inefficiencies that the CLI is not well suited for developing embedded applications. It would, however, be beneficial for developers to be able to develop embedded applications using the CLI. In order to address the issues caused by the extra layer of abstraction added by the CLI, an embedded processor is designed and implemented. This processor is capable of natively executing the CLI instruction set which effectively removes the performance problems caused by the extra layer of abstraction.

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 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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.635
Threshold uncertainty score0.147

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 science0.0010.000
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 head0.005
GPT teacher head0.310
Teacher spread0.305 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations1
Published2007
Admission routes1
Has abstractyes

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