MétaCan
Menu
Back to cohort
Record W2729527457 · doi:10.1111/cgf.12957

Front Matter

2016· paratext· en· W2729527457 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

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.Our emphases on generality, efficiency, and correct qualitative behavior set us apart from many related fields, and make for a particular kind of value we bring to varied applications.I will explore the push and pull between research in rendering and its applications -both to creating virtual worlds and to making real things -and the value of good models of the real world.Along the way I will discuss some of my own research in modeling materials for graphics, and spend some time looking for -though perhaps not finding -the secrets of selecting successful research directions.The talk is aimed primarily at students starting careers in graphics, but I hope everyone can find something of interest to take away.

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 categoriesMeta-epidemiology (narrow), Insufficient 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: Methods · Consensus signal: none
Teacher disagreement score0.680
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0050.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.026

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