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Record W2621285828 · doi:10.1002/sdtp.11687

36‐2: <i>Invited Paper</i> : Lasers, Lamps, or Phosphors – Choices for the Future of Digital Cinema

2017· article· en· W2621285828 on OpenAlexaff
M. J. Perkins, Alen Koebel

Bibliographic record

VenueSID Symposium Digest of Technical Papers · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced Optical Imaging Technologies
Canadian institutionsChristie (Canada)
Fundersnot available
KeywordsMovie theaterMainstreamPhosphorLaserSet (abstract data type)Projection (relational algebra)Computer scienceMultimediaOpticsComputer graphics (images)Engineering physicsArtEngineeringOptoelectronicsVisual artsMaterials sciencePhysicsPolitical science

Abstract

fetched live from OpenAlex

The first generation of digital‐cinema projectors has now been deployed into the majority of movie theaters around the world. The illumination technology used for that first generation was xenon lamps. When that choice was made xenon was the only viable technology that could achieve digital cinema’s goals. Today, cinema has a new set of challenges and a new set of technologies to choose from. Now that laser and laser‐phosphor are mainstream illumination technologies cinema‐projection engineers have an entirely new set of design decisions to make.

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)0.001
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.042
Threshold uncertainty score0.142

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0060.002
Open science0.0010.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0420.025

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 head0.009
GPT teacher head0.247
Teacher spread0.238 · 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
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

Citations3
Published2017
Admission routes1
Has abstractyes

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