MétaCan
Menu
Back to cohort
Record W4247615369 · doi:10.1109/mmm.2014.2309399

2013 MTT-S Graduate Student Fellowship Awards [Education News]

2014· article· en· W4247615369 on OpenAlexaff
Giovanni Crupi, Roger Kaul, Changzhi Li, Dominique Schreurs

Bibliographic record

VenueIEEE Microwave Magazine · 2014
Typearticle
Languageen
FieldEngineering
TopicWireless Body Area Networks
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsGraduate studentsLibrary scienceMedical educationEngineeringGraduate researchEngineering managementPolitical scienceComputer scienceMedicine

Abstract

fetched live from OpenAlex

The IEEE Microwave Theory and Techniques Society (MTT-S) Graduate Student Fellowship Awards are sponsored by the MTT-S for the purpose of encouraging and supporting graduate students from around the world interested in pursuing the field of microwave engineering. The graduate fellowship recipients receive an award of US$6,000 to support their research activities, which is presented at the annual IEEE MTT-S International Microwave Symposium (IMS). Supplemental travel funding is offered to the recipients to support their travel to the IMS. Ten graduate fellowships were awarded in 2013 in the general category and three in the medical applications category. In 2013, 25 applications from 12 countries were received in the general category, and eight applications from four countries were received in the medical applications category. The applications were excellent and represented some of the best research being conducted around the world. Even with the additional medical applications award, the success rate was only about 40% due to the large number of submissions. The 13 awardees and their projects are described.

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)0.003
metaresearch head score (Gemma)0.007
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: Other · Consensus signal: none
Teacher disagreement score0.899
Threshold uncertainty score0.337

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.1010.099

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 head0.017
GPT teacher head0.243
Teacher spread0.226 · 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
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 abstractyes

Explore more

Same venueIEEE Microwave MagazineSame topicWireless Body Area NetworksFrench-language works237,207