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Record W2065656952 · doi:10.1145/2037826.2037846

Processing.js

2011· article· en· W2065656952 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicComputational Physics and Python Applications
Canadian institutionsSeneca Polytechnic
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceJavaScriptUnobtrusive JavaScriptJavaPlug-inParsingRendering (computer graphics)Web applicationSketchVisualizationWorld Wide WebProgramming languageRich Internet applicationComputer graphics (images)Artificial intelligence

Abstract

fetched live from OpenAlex

The Processing language [Casey Reas 2007], first introduced by Ben Fry and Casey Reas in 2001, is a simple, elegant language for data visualization that is already being used by artists, educators and commercial media groups to produce rich graphical content called sketches. Because Processing is implemented in Java, delivering Processing sketches via a web page requires the user to install a Java plug-in. Processing.js, in comparison, is an open source, cross browser JavaScript port of the Processing language; it translate Processing sketches into JavaScript using the <canvas> element for rendering. No additional plug-ins are required to view a Processing sketch delivered with Processing.js. Furthermore, Processing.js is much more than just a Processing parser written in JavaScript: it enables the embedding of other web technologies into Processing sketches and vice versa. Processing.js seamlessly integrates web technologies with the Processing language to provide an excellent framework for rich multimedia web applications.

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.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.817
Threshold uncertainty score0.268

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 science0.0000.000
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 head0.041
GPT teacher head0.257
Teacher spread0.216 · 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