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2020· article· en· W3112859620 on OpenAlexaff
Zhi Ning Chen, Wei Hong, Zhizhang Chen, Qing Liu, Quan Xue

Bibliographic record

Venue2020 9th Asia-Pacific Conference on Antennas and Propagation (APCAP) · 2020
Typearticle
Languageen
FieldEngineering
TopicSatellite Communication Systems
Canadian institutionsDalhousie University
Fundersnot available
KeywordsSession (web analytics)ChinaEvent (particle physics)Library scienceCoronavirus disease 2019 (COVID-19)EngineeringPandemicTelecommunicationsPolitical scienceEngineering managementComputer scienceWorld Wide WebLawMedicine

Abstract

fetched live from OpenAlex

The 2020 9th Asia-Pacific Conference on Antennas and Propagation (APCAP2020), originally planned to be held in Xiamen, is now changed to be a virtual event due to the Covid-19 pandemic which is affecting every aspect of our society. The change is made since the health and safety of all participants is our top priority. The organizing committee has put together a comprehensive inline technical program to facilitate the exchange of information, progress, and advancements in the research and development of innovative antenna technology and radio wave propagation. APCAP2020 is organized by Xiamen Institute of Electronics (XMIE) and Antenna Society of the Chinese Institute of Electronics (CIE-ANT), co-organized by Xiamen University, Southeast University, South China University of Technology, Fuzhou University, Huaqiao University, and JiMei University. We would like to take this opportunity to express our sincere appreciation to the leading scientists and engineering professionals, including session organizers and all the authors, for their valuable contributions and strong supports to this conference. Our sincere thanks also go to the Technical Program Committee, the Organizing Committee and the sponsors for their time and efforts in making this conference valuable, memorable, enjoyable, and successful.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.630
Threshold uncertainty score0.828

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.042
GPT teacher head0.248
Teacher spread0.206 · 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 teacher head, 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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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