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Record W2989871442 · doi:10.1002/jsid.858

Model for assessing organic light‐emitting diode display lifetime across applications

2019· article· en· W2989871442 on OpenAlexaff
Michael E. Miller, Jérémie Gerhardt, Hyunjin Yoo, Tara Akhavan

Bibliographic record

VenueJournal of the Society for Information Display · 2019
Typearticle
Languageen
FieldEngineering
TopicGreen IT and Sustainability
Canadian institutionsOralys (Canada)
Fundersnot available
KeywordsOLEDLuminanceDiodeComputer scienceOptoelectronicsDisplay devicePower consumptionPower (physics)Materials sciencePhysicsComputer vision

Abstract

fetched live from OpenAlex

Abstract A model for assessing organic light‐emitting diode (OLED) display lifetime is developed and discussed for estimating OLED display lifetime in various applications. The lifetime model extends existing stretched exponential models of luminance decay for OLED devices to permit this decay to be estimated as a function of time and current density. This extended model is illustrated within an application to assess the power consumption and luminance decay of diodes within an OLED display. Various metrics of display lifetime are discussed with the aim of developing methods to assess the perceived lifetime of an OLED display to global and local luminance decay mechanisms. Finally, these metrics are applied to illustrate the performance of the model for assessing the impact of an image processing algorithm on OLED display lifetime.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.007
GPT teacher head0.252
Teacher spread0.245 · 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 designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations1
Published2019
Admission routes1
Has abstractyes

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