MétaCan
Menu
Back to cohort
Record W2952476589 · doi:10.1109/mce.2019.2905485

Next-Generation Video Network Design Tenets: Building a Better Video Delivery Service

2019· article· en· W2952476589 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Consumer Electronics Magazine · 2019
Typearticle
Languageen
FieldComputer Science
TopicVideo Coding and Compression Technologies
Canadian institutionsTelus (Canada)
Fundersnot available
KeywordsComputer scienceNext-generation networkVideo on demandService (business)Computer networkTelecommunicationsMultimediaWorld Wide WebThe InternetBusiness

Abstract

fetched live from OpenAlex

Traditional video delivery platforms are optimized to broadcast scheduled programs. However, the viewing habits of TV consumers are changing. People of various ages, genders, and cultures with different interests, desires, and schedules are increasingly demanding personalized service. Such demands cannot be met in an effective, timely, and cost-efficient manner using traditional video delivery platforms. An entirely new approach is needed, one that enables reuse of infrastructure, is based on open interfaces, and uses the cloud to deliver scalable, virtualized, distributed, federated services. This article explores the drivers behind deploying such platforms and identifies five design tenets that TV network operators follow when building such next-generation video delivery platforms.

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)0.001
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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.714
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.036
GPT teacher head0.241
Teacher spread0.206 · 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