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Record W4251502439 · doi:10.1002/9781119560050.ch1

Introduction to Java

2018· other· en· W4251502439 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typeother
Languageen
FieldComputer Science
TopicDistributed and Parallel Computing Systems
Canadian institutionsnot available
Fundersnot available
KeywordsReal time JavaJava annotationJavastrictfpJava CardComputer scienceJava appletJava API for XML-based RPCGenerics in JavaJava concurrencyJava Modeling LanguageProgramming languageOperating system

Abstract

fetched live from OpenAlex

Java is a high-level, object-oriented, general-purpose programming language that was originally developed by James Gosling, a Canadian computer scientist, at what was then Sun Microsystems, in the U.S. state of California in 1991. This chapter introduces the Java programming language, including its history, versions, and the four Java platform editions. They are four Java platform editions: Java Card for smartcards, Java Micro Edition for mobile devices, Java Standard Edition for standard personal computers, and Java Enterprise Edition for large distributed enterprise or Internet environments. Java Spring is the most popular development framework for creating Java enterprise applications. The advantages of Java are free cost, simplicity, platform independence, object orientation, security, and multithreading, networking, mobile development and enterprise development. Oracle offers a range of Java certificates, which can be generally divided into two levels, Associate and Professional.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.468
Threshold uncertainty score0.999

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)0.0020.023

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 head0.009
GPT teacher head0.235
Teacher spread0.225 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2018
Admission routes1
Has abstractyes

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