Research Showcase Abstracts
Bibliographic record
Abstract
The 2022 Dietitians of Canada (DC) National Conference brought together our nutrition and dietetics community for a successful virtual event on September 15 & 16, 2022. We have had three virtual conferences due to the COVID-19 pandemic, but that has not prevented us from sharing our experiences and research. This year the Canadian Foundation for Dietetic Research (CFDR) showcased a wide variety of experience sharing and research abstracts through the online conference platform. There were 33 Early Bird (EB) research abstracts, in which five were selected for live presentations during the conference, and ten Late Breaking research abstracts. Thirty-one posters were presented virtually at the conference. Thank you for all of the abstract submissions! A sincere thank you to the Abstract Review Committee members for their support, dedication and commitment. Early Bird Abstract Review Committee: Susan Campisi (University of Toronto); Pauline Darling (University of Ottawa); Andrea Glenn (University of Toronto); Mahsa Jessri (University of British Columbia); Shelley Vanderhout (University of Toronto). Late Breaking Abstract Review Committee: Lesley Andrade (University of Waterloo); Carla D’Andreamatteo (Consultant, Winnipeg); Pauline Darling (University of Ottawa); Laura Forbes (University of Guelph); Billie Jane Hermosura (University of Ottawa); Christine Nash (University Health Network). Thanks to the DC Conference team, all of the moderators and conference attendees for supporting the virtual and poster research presentations. Please consider submitting an abstract for the 2023 CFDR Research Showcase. Looking forward to seeing all of you at the 2023 Dietitians of Canada Conference in Montreal, QC, from May 24–26, 2023. Warm regards, Christina Lengyel, PhD, RD Chair, 2022 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 | 0.011 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.491 | 0.287 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".