MétaCan
Menu
Back to cohort
Record W4239000808 · doi:10.1002/9781118798706.hdi060

Digital Television

2015· other· en· W4239000808 on OpenAlexaff
Stefan Mozar, Rongshan Yu

Bibliographic record

Venuenot available
Typeother
Languageen
FieldComputer Science
TopicVideo Coding and Compression Technologies
Canadian institutionsCMC Microsystems (Canada)
Fundersnot available
KeywordsDigital televisionOrthogonal frequency-division multiplexingComputer scienceMultiplexingSIGNAL (programming language)Transmission (telecommunications)Electronic engineeringTelecommunicationsDigital signal processingDigital signalHigh-definition televisionComputer hardwareEngineeringChannel (broadcasting)

Abstract

fetched live from OpenAlex

Abstract This chapter provides a summary of a Digital TV (DTV), from a signal processing point of view. After an overview of DTV, video and audio compression are covered. This is followed by multiplexing of the video, audio and data signals to form a transportation stream. In order to be able to receive a digital signal, forward error correction is required. The next step in preparing a signal for transmission is to modulate it. Orthogonal Frequency Division Multiplexing (OFDM) is used in DTV systems. The benefits of OFDM are discussed. The signal path in a receiver is briefly covered, as well as a summary of trends in television receiving 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.

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)0.000
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: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.278
Threshold uncertainty score0.931

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.2780.140

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 head0.022
GPT teacher head0.248
Teacher spread0.226 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

Explore more

Same topicVideo Coding and Compression TechnologiesFrench-language works237,207