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Record W3150385764 · doi:10.1109/pacrim.2017.8121878

Program committee members

2017· article· en· W3150385764 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Data Storage Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsChinaLibrary scienceGeographyArchaeology

Abstract

fetched live from OpenAlex

Xianghui Xie, JiangNan Institute of Computing Technology, China Xiaowu Chen, Beihang University, China Xiaoshe Dong, Xian Jiaotong University, China Wei Chen, Microsoft Research Asia, China Zhichen Xu, Yahoo! Inc., USA Yafei Dai, Peking University, China Yuan Xue, Vanderbilt University, USA Beixing Deng, Tsinghua University, China Yu Chen, Microsoft Research Asia, China Mingzhong Xiao, Peking University, China Wei Zou, Peking University, China Wanlei Zhou, Deakin University, Australia Yiming Hu, University of Cincinnati, USA Xiaofei Liao, Huazhong University of Sci. & Tech., China Lidong Zhou, Microsoft Research, USA Shuigeng Zhou, Fudan University, China Hongli Zhang, Harbin Institute of Technologym, China Yingfei Dong, University of Hawaii, USA Shoubing Dong, Huanan University of Sci. & Tech., China Hongling Yu, Tsinghua University, China Qianni Deng, Shanghai Jiaotong University, China Yingchun Yang, Zhejiang University, China Mark Baker, University of Portsmouth, UK Jinjun Chen, Swinburne University of Technology, Australia Ewa Deelman, University of Southern California, USA Schahram Dustdar, Vienna University of Technology, Austria Yushun Fan, Tsinghua University, China Geoffrey Fox, Indiana University, USA Wolfgang Gentzsch, D-Grid Germany, and RENCI, USA Karthik Gomadam, University of Georgia, USA Andrzej Goscinski, Deakin University, Australia Yanbo Han, CAS, China Ken Hawick, Massey University, New Zealand Jane Hunter, The University of Queensland, Australia Hai Jin, Huazhong University of Science and Technology, China Minglu Li, Shanghai Jiaotong University, China Omer F. Rana, University of Cardiff, UK Paul Roe, Queensland University of Technology, Australia Feiyue Wang, The University of Arizona, USA Andrew Wendelborn, University of Adelaide, Australia Mengchu, Zhou, The New Jersey Institute of Technology, NJIT, USA Albert Zomaya, The University of Sydney, Australia Subhash Bhalla, The University of Aizu, Japan Jiannong Cao, Hong Kong Polytechnic University, China Daoxu Chen, Nanjing University, China Pao-Ann Hsiung, Chung Cheng University, Taiwan Lijun Chen, Nanjing University, China Javier Garcia Villalba, Complutense University of Madrid, Spain Ching-Hsien Hsu, Chung Hua University, Taiwan Baocai Yin, Beijing Polytechnic University, China Antonio Puliafito, University of Messina, Italy Bin Xiao, Hong Kong Polytechnic University, China Incheon Paik, The University of Aizu, Japan Mieso Denko, University of Guelph, Canada

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)0.007
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.347
Threshold uncertainty score0.495

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0030.001
Scholarly communication0.0080.005
Open science0.0030.005
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.6530.606

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 head0.032
GPT teacher head0.317
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

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".

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Citations0
Published2017
Admission routes1
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