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Record W4242023430 · doi:10.1109/icde.2019.00006

Organizing Committee

2019· article· en· W4242023430 on OpenAlexaff
Christian S. Jensen, Lionel M. Ni, Xuemin Lin, Divesh Srivastava, Luna Xin, Wolfgang Lehner, Jingren Zhou, Yoshiharu Ishikawa, Qiong Luo, Torben Pedersen, Jeffrey Xu Yu, Nikolaus Augsten, Helen Zi, Huang Huang, Mohamed F. Mokbel, Tova Milo, Jun Yang, Xiaofang Zhou, Chair Finance, Leong Hou U, Wenjie Zhang, Feifei Li, Timos Sellis, Publicity Co, Lukasz Golab, Lei Zou, Lijun Chang, Muhammad Aamir Cheema, Lei Chen, Hong Kong, Zhiguo Gong, Ying Zhang, Selçuk Candan, Sang Cha, Kam‐Fai Wong, Reynold Cheng, Andrew Jiang, Macau Convention, Jingjing Lin, Shirley W. I. Siu, Derek Wong, Jianliang Xu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHuman auditory perception and evaluation
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.

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)0.010
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation 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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.737
Threshold uncertainty score0.879

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0030.001
Scholarly communication0.0090.002
Open science0.0030.003
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.2630.329

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 head0.012
GPT teacher head0.213
Teacher spread0.201 · 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.

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
Published2019
Admission routes1
Has abstractyes

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