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Record W2729527457 · doi:10.1111/cgf.12957

Front Matter

2016· paratext· en· W2729527457 on OpenAlexfundno aff

Bibliographic record

VenueComputer Graphics Forum · 2016
Typeparatext
Languageen
FieldComputer Science
TopicComputer Graphics and Visualization Techniques
Canadian institutionsnot available
FundersUniversity of California, San DiegoUniversidad de ZaragozaUniversité de MontréalTechnische Universität BraunschweigUniversité de LyonTechnische Universität BerlinSapienza Università di RomaLunds UniversitetUniversity of BernTechnische Universiteit DelftInstitut national de recherche en informatique et en automatique (INRIA)Dartmouth CollegeÉcole Polytechnique Fédérale de LausanneAcademy of Motion Picture Arts and SciencesČeské Vysoké Učení Technické v PrazeUniversity of TorontoUniversity College LondonZhejiang UniversityImperial College LondonUniverzita Karlova v PrazeNvidia
KeywordsComputer scienceComputer graphics (images)Front (military)Geology

Abstract

fetched live from OpenAlex

Computer graphics is a unique and fascinating field – it draws people with many goals but with a shared passion for building models and simulations that exhibit the complexity, simplicity, and beauty of our real world.

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 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 score0.075
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0100.005
Open science0.0020.003
Research integrity0.0060.003
Insufficient payload (model declined to judge)0.9250.927

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 head0.015
GPT teacher head0.274
Teacher spread0.259 · 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; the direct Gemma label and the distilled Codex classifier 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
Published2016
Admission routes1
Has abstractyes

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