MétaCan
Menu
Back to cohort
Record W4399291617 · doi:10.47392/irjaem.2024.0265

Unleashing the Power of Graphics: A Comprehensive Exploration of WebGPU

2024· article· en· W4399291617 on OpenAlexaff
S. Sarathi, P. S. Varshiga

Bibliographic record

VenueInternational Research Journal on Advanced Engineering and Management (IRJAEM) · 2024
Typearticle
Languageen
FieldEngineering
Topic3D Modeling in Geospatial Applications
Canadian institutionsArtificial Intelligence in Medicine (Canada)
Fundersnot available
KeywordsGraphicsPower (physics)Computer graphics (images)Computer sciencePhysics

Abstract

fetched live from OpenAlex

WebGPU represents a major breakthrough in web graphics technology, providing a high-performance, cross-platform solution for harnessing GPU capabilities in web browsers This paper explores the complexities of WebGPU, exploring its design, its characteristics, potential applications, and implications for future web development Through a comparative comparison with APIs, this paper aims to provide readers with a comprehensive understanding of WebGPU and its transformational impact on web-based graphics rendering

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)0.001
metaresearch head score (Gemma)0.004
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: Review · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0010.003
Scholarly communication0.0070.007
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.044
GPT teacher head0.333
Teacher spread0.290 · 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
GenreReview

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

Citations2
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueInternational Research Journal on Advanced Engineering and Management (IRJAEM)Same topic3D Modeling in Geospatial ApplicationsFrench-language works237,207