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Record W2970901164 · doi:10.26685/urncst.159

The 2019 Canadian Undergraduate Computer Science Conference

2019· article· en· W2970901164 on OpenAlexafffundabout
Aislyn A. Laurent, Noah Campbell, Pooya Moradian Zadeh, Austin Formagin, Bryce Hughson, Ryan Lebeau, David W. Worley

Bibliographic record

VenueUndergraduate Research in Natural and Clinical Science and Technology (URNCST) Journal · 2019
Typearticle
Languageen
FieldDecision Sciences
TopicScientific Computing and Data Management
Canadian institutionsUniversity of Windsor
FundersUniversity of Windsor
KeywordsExcellenceUndergraduate researchWork (physics)Multidisciplinary approachField (mathematics)Engineering ethicsComputer scienceLibrary scienceMathematics educationMedical educationEngineeringSociologyPolitical sciencePsychologySocial science

Abstract

fetched live from OpenAlex

The Canadian Undergraduate Computer Science Conference (CUCSC) is an annual, national, and bilingual gathering of students in computer science and affiliated multidisciplinary fields run by and for undergraduate students. Since 2015, CUCSC has traditionally brought together 150 of the brightest computer science students from across Canada to connect with leaders in academia and industry working at the cutting edge of technology. Centered around computer science research excellence by Canadian undergraduate students and their research teams, CUCSC provides a platform to undergraduate research relating to computer science or technology from any field. Beginning in 2019, CUCSC has offered for the first time the opportunity for all delegates to have their work published, in the interest of furthering our goal of student success.

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)0.008
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 score0.966
Threshold uncertainty score0.583

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0050.002
Scholarly communication0.0070.002
Open science0.0030.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.1600.046

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 head0.137
GPT teacher head0.465
Teacher spread0.328 · 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

Citations0
Published2019
Admission routes3
Has abstractyes

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