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Record W4242819519 · doi:10.1002/app.46804

Editorial Board, Aims & Scope, Table of Contents

2019· paratext· en· W4242819519 on OpenAlexaff
Stefan Spiegel, Conor Doss, Jie Cai, Emily Hu, Ying Jia, Jenny Mahoney, Mara Staffilani, Bo Weng, Marc Zastrow, Carla Amador, Lisa Founding, Herman Consulting, Eric Baer, Subramanian Iyer, Brian Knapp, Yuzhong Wang, Christoph Weder, Andrew K. Whittaker, Paula M. Wood‐Adams, Kenneth J. Wynne, Lina Zhang, Liqun Zhang, Danielle Lupo, Marisa Taylor, Lan Shen, Pu Li, And Zhang, Shaojun Guo, You Lv, Yang Chen, Shengbao Cai, Yogesh N. Marathe, Arun Torris, C. Ramesh, Manohar V. Badiger, Chenyu Zhang, Yibing Ma, Bo Feng, Z Han, Zhibing Zhang, Wang Liang, K Wang, Lifang Yang, James Runt, M.-C Kuo, Ke Huang, Jen‐Taut Yeh, Young‐Hee Ryu, I Kim, Sung‐Hoon Kim, Wenjun Li, Min Chen, Yi Yang, Ding Yuan, Yutao Ren, X. Cai, L. Xia, Jianping Song, Han Wang, Zhengyan Kan, Theresa Liang, А. I. Isayev, Junge Zhi, Qingyuan Wang, M Zhang, M Li, B De Lima, N. Marques, Marcos A. Villetti, Robert S. Balaban, Li Ge, N. Fan, Zhou Ye, Min Xia, Y Ye, D. S. Moreno, Clodoaldo Saron, Subrata Saha, Arnab Bhowmick, Xing Wei, Hao Hu, Xiaomian Li, Qiang Yin, Liqiang Fan, Yanbin Huang, Jidong Yang, Supratik Bhattacharyya, Vivekanda Lodha, Sudip Dasgupta, Rajendrani Mukhopadhyay, Abhijit Guha, Palash Sarkar, Tapan Saha

Bibliographic record

VenueJournal of Applied Polymer Science · 2019
Typeparatext
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsConcordia University
Fundersnot available
KeywordsScope (computer science)Table of contentsTable (database)Editorial boardCitationComputer scienceLibrary scienceWorld Wide WebInformation retrievalDatabaseProgramming language

Abstract

fetched live from OpenAlex

Effects of content of shear thickening fluid and particle size of silica

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)0.003
metaresearch head score (Gemma)0.010
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 score0.765
Threshold uncertainty score0.785

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.002
Science and technology studies0.0020.001
Scholarly communication0.0090.003
Open science0.0020.001
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.2350.145

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 head0.014
GPT teacher head0.249
Teacher spread0.235 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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