Research Showcase Early Bird Abstracts
Bibliographic record
Abstract
The 2023 Dietitians of Canada (DC) National Conference held on 25–26 May 2023 was our first in-person conference since June 2019 and celebrated our unity and resiliency as a profession. It was amazing! The Canadian Foundation for Dietetic Research (CFDR) showcased a diverse array of experience sharing and research abstracts through oral and poster presentations, and virtual sessions. There were 23 Early Bird (EB) research abstracts, in which 10 were selected for in-person presentations during the conference, and 19 Late Breaking research abstracts which were posters. Thank you to all who submitted abstracts! Thanks to the dedication and commitment of the Abstract Review Committee members. Early Bird Abstract Review Committee: Susan Campisi (University of Toronto); Andrea Glenn (University of Toronto); Mahsa Jessri (University of British Columbia); Louise St-Denis (University of Montreal). Late Breaking Abstract Review Committee: Lesley Andrade (University of Waterloo); Carla D’Andreamatteo (Consultant, Winnipeg); Laura Forbes (University of Guelph); Billie Jane Hermosura (University of Ottawa); Christine Nash (University Health Network); Louise St-Denis (University of Montreal). Thanks to the CFDR Board, DC Conference team, digital partners, all of the moderators, and conference attendees for supporting the research presentations. Warm regards, Christina Lengyel, PhD, RD Chair, 2023 EB/LB Abstract Committees Professor Food and Human Nutritional Sciences University of Manitoba Ravi Sidhu Managing Director Development & Operations CFDR
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.214 | 0.114 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".