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Record W4237769019 · doi:10.1109/cicc.2013.6658396

Steering committee

2013· article· en· W4237769019 on OpenAlexfundno aff
J. R. Snyder, Marvell Semiconductor, Rakesh Patel, T. André, Everspin Technologies, Irfan Ullah Khan, Altia Systems, Ramesh Harjani, Philippe Jansen, General Chair, Texas Instruments, Howard Luong, Hong Jin Kong, Trent Mcconaghy, Solido Design, Panel Kimotam, Alessandro Picovaccari, Ken Suyama, Ron Kapusta, Pavan Kumar Hanumolu, E. Naviasky, Ron Cadence, Analog Kapusta, Yuji Devices, Renesas Nakajima, Yusuf Haque, Xicheng Jiang, Mohammad Ranjbar, Cirrus Logic, John McNeill

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicDiverse Scientific and Economic Studies
Canadian institutionsnot available
FundersUniversity of California, DavisUniversity of MinnesotaAuburn UniversityUniversity of WaterlooCase Western Reserve UniversityKorea Advanced Institute of Science and TechnologyMcGill UniversityArizona State UniversityOregon State UniversitySeoul National UniversityCisco SystemsIntel Corporation
KeywordsComputer scienceSteering committeeEngineeringEngineering management

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)0.035
metaresearch head score (Gemma)0.062
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: Empirical · Consensus signal: none
Teacher disagreement score0.357
Threshold uncertainty score0.917

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0350.062
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.002
Science and technology studies0.0030.001
Scholarly communication0.0080.003
Open science0.0040.006
Research integrity0.0070.005
Insufficient payload (model declined to judge)0.3570.301

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 head0.041
GPT teacher head0.179
Teacher spread0.138 · 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
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".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractno

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