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Record W4205655262 · doi:10.1109/jproc.2021.3109706

Proceedings of the IEEE publication information

2021· article· en· W4205655262 on OpenAlexaff
D.J. Allstot, Moeness G. Amin, Göran Andersson, Ronald Arkin, John Baillieul, Claudio A. Cañizares, Gert Cauwenberghs, Jocelyn Chanussot, Raja Chatila, Hsiao‐Hwa Chen, Diane J. Cook, Jack Dongarra, John S. Duncan, Michael Fang, Georges Gielen, Maya Gokhale, Lawrence Hall, Christofer Hierold, Adrian Ionescu, Pramod P. Khargonekar, Agnieszka Konczykowska, Khaled B. Letaief, James Lyke, Manish Parashar, Marios M. Polycarpou, Heather K. Vincent, Catherine Rosenberg, Robert Schober, Mohammad Shahidehpour, Manos M. Tentzeris, Isabel Trancoso, Jun Yoo, C Patrick, Stephen Welby, Cherif Amirat, Chris Brantley, Steven Heffner, Karen Hawkins, Donna Hourican, Dawn Melley, Kevin Lisankie, Peter Tuohy, Jeffrey Cichocki, Neelam Khinvasara, Larry Hall, Sergio Benedetto, Vice Chair, Abbott Brian Blake, Eddie Custovic, Stephen Dukes, Stefano Galli, Jean‐Luc Gaudiot, Ekram Hossain, Sundaram Ramesh, Annette Reilly, Sorel Reisman, Gianluca Setti, Gaurav Sharma, Sharad Sinha, Maria Elena Valcher, Peter J. Winzer, Wai Choong, Lawrence, Wong, Steve Yurkovich, Cecelia Jankowski, Konstantinos Karachalios, Sophia Muirhead, Jamie Moesch, Thomas Siegert, Mary Ward-Callan, Vaishali Damle, Jun Sun, Patrick Kempf, Joanna Gojlik

Bibliographic record

VenueProceedings of the IEEE · 2021
Typearticle
Languageen
FieldComputer Science
TopicInternet of Things and AI
Canadian institutionsCanadian Standards Association
Fundersnot available
KeywordsComputer scienceLibrary scienceWorld Wide WebInformation retrieval

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.377
Threshold uncertainty score0.538

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0050.004
Science and technology studies0.0010.000
Scholarly communication0.0060.004
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.6230.576

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 head0.008
GPT teacher head0.202
Teacher spread0.194 · 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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractno

Explore more

Same venueProceedings of the IEEESame topicInternet of Things and AIFrench-language works237,207