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Record W3130202082 · doi:10.26685/urncst.241

2020-2021 IgNITE Medical Case Competition: Regenerative Medicine

2021· article· en· W3130202082 on OpenAlexaffabout
Dejan Bojic, Bianka Bezuidenhout, Hertek Gill

Bibliographic record

VenueUndergraduate Research in Natural and Clinical Science and Technology (URNCST) Journal · 2021
Typearticle
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCompetition (biology)Competitor analysisTheme (computing)Public relationsReading (process)Medical educationPolitical sciencePsychologyMedicineMarketingBusinessComputer science

Abstract

fetched live from OpenAlex

The IgNITE Medical Case Competition is an annual research case competition organized by students at the University of Ottawa. Our mission is to provide high school and university students the opportunity to gain valuable research experience while networking with industry professionals. Each year students, in teams of 1-4, are paired with an experienced mentor to develop and present a novel research proposal within the specified theme of the competition. During the competition, students are taught the fundamental principles underlying three lab techniques which they can then use in their proposal or their future research career. This year’s theme was Regenerative Medicine and competitors learned about Immunofluorescence, Western Blot, and CRISPR-Cas9. In 2020-2021 the IgNITE community grew internationally with 570 high school and university students across the world participating in the competition. In this booklet we present the Top 40 teams and invite you to visit our website (www.ignitecompetition.org) to watch their pitch proposal videos. We hope you enjoy reading through some of this year’s top proposals and invite you to join our growing community.

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)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.855
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.003
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 head0.040
GPT teacher head0.389
Teacher spread0.349 · 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 designOther design
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
Published2021
Admission routes2
Has abstractyes

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