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Record W4255368586 · doi:10.17975/sfj-2020-008

Undergraduate Engineering Research Day 2020

2020· article· en· W4255368586 on OpenAlexaffvenueabout
Ahmed Abdelmoneim, Gerard O’Leary, Roman Genov, Edward S. Rogers, Anaqi Afendi, Peifeng Xu, Krishna Mahadevan, Brohath Amrithraj, Ariel Chan, Julianne Attai, Katherine Whelan, Patricia Sheridan, Joseph Bellissimo, Radhakrishnan Mahadevan, Michael De Biasio, Kramay Patel, Taufik A. Valiante

Bibliographic record

VenueSTEM Fellowship Journal · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsToronto Western HospitalUniversity of Toronto
Fundersnot available
KeywordsUndergraduate researchEngineering researchEngineering managementEngineeringEngineering ethicsEngineering educationMedical educationMedicine

Abstract

fetched live from OpenAlex

The University of Toronto’s Undergraduate Engineering Research Day (UnERD) is an annual conference aimed at providing an opportunity for undergraduate engineering students to showcase their research to industry professionals and fellow students, inspiring the exchange of innovative solutions across a wide breadth of global challenges.

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)0.003
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: Other
Teacher disagreement score0.437
Threshold uncertainty score0.803

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0060.001
Open science0.0020.006
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.4370.301

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 head0.133
GPT teacher head0.382
Teacher spread0.249 · 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
Published2020
Admission routes3
Has abstractyes

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