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Record W3204572223 · doi:10.25011/cim.v44i3.37273

Fall 2021: Clinician Investigator Trainee Association of Canada (CITAC)

2021· article· en· W3204572223 on OpenAlexaffvenueabout
Melissa Phuong, Valera Castanov, Sophie Hu, Danny Jomaa, Wenxuan Wang, Heather Whittaker, Adam Pietrobon

Bibliographic record

VenueClinical and investigative medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsWestern UniversityMcGill University Health CentreUniversity of CalgaryQueen's UniversityUniversity of Ottawa
Fundersnot available
KeywordsAssociation (psychology)Medical educationContinuationFamily medicineMedicineGraduate studentsPsychologyGerontologyComputer science

Abstract

fetched live from OpenAlex

I hope you’re taking care and found some time to relax this summer. A new semester may mean a big transition—some folks are starting their graduate studies, re-entering clerkship, starting residency or entering a fellowship. For some, there will be little or no change at all; but just a continuation of one of the many phases of the physician-scientist training pathway. Whatever stage you’re at, the Clinical Investigator Trainee Association of Canada (CITAC) community is here to support and advocate for you!

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)0.011
metaresearch head score (Gemma)0.023
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: none
Teacher disagreement score0.971
Threshold uncertainty score0.955

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.023
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0190.003
Scholarly communication0.0120.003
Open science0.0040.007
Research integrity0.0190.012
Insufficient payload (model declined to judge)0.3300.126

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 head0.358
GPT teacher head0.449
Teacher spread0.091 · 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
Published2021
Admission routes3
Has abstractyes

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