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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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.962
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 teacher head, 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
Published2020
Admission routes3
Has abstractyes

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