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Record W4323306901 · doi:10.1109/mpel.2023.3237061

Explore the IEEE PELS Students and Young Professionals Committee and Get Involved [Students and Young Professionals Rendezvous]

2023· article· en· W4323306901 on OpenAlexaboutno aff
Haifah Sambo, Anshuman Sharma, Nayara B. de Freitas, Joseph P. Kozak

Bibliographic record

VenueIEEE Power Electronics Magazine · 2023
Typearticle
Languageen
FieldEngineering
TopicSystems Engineering Methodologies and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsGlobeYoung professionalMentorshipDynamismDiversity (politics)Sri lankaPolitical scienceMedical educationPublic relationsSociologySocioeconomicsPsychologyMedicineLaw

Abstract

fetched live from OpenAlex

Over the last years, the number of student and young professional members in IEEE PELS has increased. Students and young professionals now represent over 20% of the membership body and make significant contributions to the dynamism of IEEE PELS. Via the IEEE PELS Students and Young Professionals (S&YP) committee, a subgroup within the PELS membership committee, students and recent graduates in IEEE PELS host a variety of events at IEEE conferences and create activities to engage PELS members while enabling unique networking and mentorship opportunities. With volunteers all across the globe (Algeria, Brazil, Canada, Germany, India, Portugal, Spain, Sri Lanka, and the United States), the S&YP committee also establishes a global and inclusive community for engineers, researchers, academics and students in the area of power electronics. Through its endeavors, the S&YP committee aims to support the professional development of students and recent graduates, further the mission of IEEE PELS and increase the diversity of its membership and volunteering bodies.

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)0.004
metaresearch head score (Gemma)0.010
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: Other
Teacher disagreement score0.127
Threshold uncertainty score0.424

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0080.002
Scholarly communication0.0100.006
Open science0.0020.009
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.1270.072

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 head0.050
GPT teacher head0.338
Teacher spread0.288 · 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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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