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Record W4250059894 · doi:10.1109/ted.2014.2323832

IEEE Transactions on Electron Devices publication information

2014· article· en· W4250059894 on OpenAlexaff
A Wang, Peixin Yu, Junior Past, R Todi, Rajeev Jindal, Montejo Camacho, M Celik-Butler, Siew Hwa Chan, S Chung, A Deleonibus, F Escobosa, R Guarin, Shujuan Huang, Lakshminarayan M. Iyer, M Lunardi, A. Meyyappan, M. I. Nathan, Mikael Östling, T.-L Radhakrishnan, Saiyu Ren, E Saha, M Sangiorgi, Joshua Shur, D Swart, B Verret, Xu Zhao, Bin Zhao, L Lunardi, Xing Zhou, Colin C. McAndrew, G. Bersuker, Albert Chin, Giovanni Ghione, Satoru Ikeda, C. Jagadish, Christoph Jungemann, Veena Misra, Tianhua Ren, Rajendra Singh, Franky So, C. Surya, R.B. True, Amitava Chatterjee, John D. Cressler, Saurabh Sinha, Ralph V. Ford, Karen Bartleson, Jacek M. Żurada, Gary Blank, Ellen J. Yoffa, Dr Prendergast, Thomas Siegert, Business Administration, Matthew Loeb, Douglas Gorham, Eileen Lach, Shannon Johnston, Ieee-Usa Chris Brantley, Alexander Pasik, Patrick Mahoney, Peter Tuohy, Margaret Rafferty

Bibliographic record

VenueIEEE Transactions on Electron Devices · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced Semiconductor Detectors and Materials
Canadian institutionsCanadian Standards Association
Fundersnot available
KeywordsComputer scienceElectrical engineeringOptoelectronicsWorld Wide WebMaterials scienceEngineering

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)0.001
metaresearch head score (Gemma)0.001
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 score0.647
Threshold uncertainty score0.922

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.3530.158

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 head0.224
Teacher spread0.216 · 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
Published2014
Admission routes1
Has abstractno

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