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Record W4253893937 · doi:10.1007/978-1-4842-3988-9_6

JavaScript Objects

2019· book-chapter· en· W4253893937 on OpenAlexaff
Sammie Bae

Bibliographic record

VenueApress eBooks · 2019
Typebook-chapter
Languageen
FieldDecision Sciences
TopicScientific Computing and Data Management
Canadian institutionsHamilton Health Sciences
Fundersnot available
KeywordsJavaScriptUnobtrusive JavaScriptComputer scienceProgramming languageRich Internet applicationInheritance (genetic algorithm)Focus (optics)World Wide WebPhysics

Abstract

fetched live from OpenAlex

JavaScript objects are what make the JavaScript programming language so versatile. Before diving into data structures and algorithms, let’s review how JavaScript objects work. This chapter will focus on what JavaScript objects are, how they are declared, and how their properties can be changed. In addition, this chapter will cover how JavaScript classes are implemented using prototypal inheritance.

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)0.002
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: Methods · Consensus signal: none
Teacher disagreement score0.075
Threshold uncertainty score0.252

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0080.010
Open science0.0030.003
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0750.121

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 head0.191
GPT teacher head0.348
Teacher spread0.158 · 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
GenreMethods

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
Published2019
Admission routes1
Has abstractyes

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