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Record W2574770385 · doi:10.5594/m001698

Flexible SDI - The Universal Transport for Streamed Media

2016· article· en· W2574770385 on OpenAlexaff
Nigel Seth-Smith

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicMultimedia Communication and Technology
Canadian institutionsSemtech (Canada)
Fundersnot available
KeywordsComputer scienceHigh-definition televisionFlexibility (engineering)Layer (electronics)Digital televisionMultimediaOperating systemTelecommunications

Abstract

fetched live from OpenAlex

SDI was invented to carry all commonly used professional digital video formats in a single data rate of 270 Mb/s. With HDTV it added a 1.5Gb/s layer to carry all HD formats. SDI infrastructures automatically carried both 270 Mb/s and 1.5 Gb/s signals, and so were universal. In 2005 a 3Gb/s layer was added, and UHDTV has recently raised the game once again, by increasing the data rates, and also with an explosion of different formats. SDI has responded with multi-link 3G-SDI, and by adding layers at 6Gb/s and 12Gb/s, with a 24 Gb/s layer in the pipeline. This paper describes how SDI is used to transport all streamed media, from SD to UHD. In particular it highlights the use of gearbox technology for seamless zero-latency bridging between SDI layers. This flexibility allows an evolutionary move from HD to UHD without wholesale replacement of expensive and mission-critical infrastructure.

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 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.978
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.048
GPT teacher head0.327
Teacher spread0.279 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations0
Published2016
Admission routes1
Has abstractyes

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