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Record W2132492153 · doi:10.1109/fie.2010.5673314

Summer Innovation Experience for undergraduates in semiconductor technology

2010· article· en· W2132492153 on OpenAlexaboutno aff
Santosh Kurinec, S.L. Rommel, Dale E. Ewbank, Karl D. Hirschman

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNanotechnology research and applications
Canadian institutionsnot available
FundersAmerican Society for Engineering EducationNational Science Foundation
KeywordsCreativityQuarter (Canadian coin)Graduate studentsMedical educationWork (physics)Minor (academic)Engineering educationGraduate educationInternshipEngineeringEngineering managementPsychologyMedicinePolitical scienceMechanical engineering

Abstract

fetched live from OpenAlex

The President of Rochester Institute of Technology, Dr. William Destler announced in 2007 renewed emphasis on innovation and creativity in engineering education from the beginning in undergraduate and graduate education. The Microelectronic Engineering program at RIT proposed to establish a program-Summer Innovation Experience which will allow undergraduate students to participate in innovative research projects during the first and the fourth summer quarter. The program is tailored to first year students, fourth year students seeking graduate school, and students from other disciplines taking the Minor program in Microelectronics and Nanofabrication. Students work under the supervision of a faculty and team up with graduate students. The program is offered during the summer the quarter (June-August) funded through Research Experience for Undergraduates supplements on ongoing projects and industry support. Students are required to present at the institute wide Undergraduate Research Symposium held at RIT each year during the second week of August.

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)0.005
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.024
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0060.002
Scholarly communication0.0040.002
Open science0.0010.008
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0240.009

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 head0.023
GPT teacher head0.306
Teacher spread0.283 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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".

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Citations0
Published2010
Admission routes1
Has abstractyes

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