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Record W4220991117 · doi:10.26685/urncst.355

University of Ottawa Healthcare Symposium (UOHS) 2022 Pitch-O-Rama: Undergraduate Elevator Pitch Research Competition

2022· article· en· W4220991117 on OpenAlexafffundabout
Tuba Buyuktepe, Juliane Feliciano, Moatter Syed, Tiffany Yang

Bibliographic record

VenueUndergraduate Research in Natural and Clinical Science and Technology (URNCST) Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicDigital Imaging in Medicine
Canadian institutionsUniversity of Ottawa
FundersUniversity of Ottawa
KeywordsCompetition (biology)ElevatorHealth careEvent (particle physics)Panel discussionMedical educationPsychologyPublic relationsPolitical scienceSociologyLibrary scienceMedia studiesEngineeringMedicineAdvertisingComputer scienceBusiness

Abstract

fetched live from OpenAlex

The University of Ottawa Healthcare Symposium (UOHS) is a one-day undergraduate health conference that aims to increase awareness of the interdisciplinary field of health. This conference engages students’ interest in health through seminars, interactive panel discussions, and a research-based elevator pitch competition. UOHS was created twelve years ago by undergraduate students and has grown to become the University of Ottawa’s largest healthcare conference. Every year, UOHS hosts an event called the Pitch-O-Rama, during one of the conference’s seminar blocks. This event is an elevator pitch competition where individuals have the opportunity to present their healthcare-related research to an audience and panel of judges in a clear and engaging way. The goal of the Pitch-O-Rama is to have students communicate and share their scientific research with the community. The written submissions of the top 3 winners are highlighted in this abstract book. More details about UOHS can be found on our website: https://www.uohs-csuo.com/.

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)0.020
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.525
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0200.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.007
Science and technology studies0.0020.009
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.008
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 head0.057
GPT teacher head0.414
Teacher spread0.357 · 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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
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
Published2022
Admission routes3
Has abstractyes

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