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Record W2055972993 · doi:10.1145/2736084.2736091

On crowdsourced interactive live streaming

2015· article· en· W2055972993 on OpenAlexaff
Cong Zhang, Jiangchuan Liu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicImage and Video Quality Assessment
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsComputer scienceCrowdsourcingMultimediaLive streamingUser-generated contentWorld Wide WebService (business)Quality (philosophy)Social mediaBusiness

Abstract

fetched live from OpenAlex

Empowered by today's rich tools for media generation and collaborative production, the multimedia service paradigm is shifting from the conventional single source, to multi-source, to many sources, and now toward crowdsource. Such crowdsourced live streaming platforms as Twitch.tv allow general users to broadcast their content to massive viewers, thereby greatly expanding the content and user bases. The resources available for these non-professional broadcasters however are limited and unstable, which potentially impair the streaming quality and viewers' experience. The diverse live interactions among the broadcasters and viewers can further aggravate the problem.

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)0.005
metaresearch head score (Gemma)0.014
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 score0.013
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0020.003
Scholarly communication0.0030.006
Open science0.0030.007
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0130.003

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 head0.049
GPT teacher head0.334
Teacher spread0.285 · 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

Citations118
Published2015
Admission routes1
Has abstractyes

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