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Record W1991946596 · doi:10.1109/tbc.2014.2310067

FOBTV: Worldwide Efforts in Developing Next-Generation Broadcasting System

2014· article· en· W1991946596 on OpenAlexaff
Wenjun Zhang, Yiyan Wu, Namho Hur, Tetsuomi Ikeda, Pingjian Xia

Bibliographic record

VenueIEEE Transactions on Broadcasting · 2014
Typearticle
Languageen
FieldEngineering
TopicTelecommunications and Broadcasting Technologies
Canadian institutionsCommunications Research Centre Canada
FundersNational Key Research and Development Program of ChinaNational High-tech Research and Development ProgramHigher Education Discipline Innovation ProjectShanghai Jiao Tong UniversityNational Natural Science Foundation of China
KeywordsStandardizationBroadcasting (networking)TelecommunicationsBroadcast engineeringComputer scienceBroadcast lawRadio broadcastingEngineering managementEngineeringCommercial broadcastingComputer security

Abstract

fetched live from OpenAlex

The challenges facing the terrestrial broadcast industry are to how more efficiently and effectively use the scarce spectrum to deliver the vast amount of media data to the general public. Future of Broadcast Television Initiative (FOBTV) was founded by the broadcasters, manufacturers, network operators, standardization organizations, research institutes, and universities around the world, aiming at better solving these problems through global collaboration. Since its establishment, use cases for the future broadcast applications have been extensively collected and classified. Potential technologies applied for the summarized new application scenarios have been carefully studied and the most effective ones are highlighted. Moreover, FOBTV is trying to develop a layered model for the next-generation broadcasting system and taking great efforts toward the ultimate goal of a global harmonized terrestrial broadcasting system.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.044
GPT teacher head0.233
Teacher spread0.189 · 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
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

Citations31
Published2014
Admission routes1
Has abstractyes

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