CFDR Research Showcase: Early Bird Abstracts
Bibliographic record
Abstract
Given the pandemic challenges we experienced over the last year, the 2021 Dietitians of Canada (DC) National Conference from May–June brought our dietetic community together from all across Canada and the world. This year the Canadian Foundation for Dietetic Research (CFDR) showcased another successful event with novel, relevant, timely research projects via the OnAIR Virtual Event Portal. Twenty-four research abstracts were submitted and reviewed by the Early Bird Abstract Review Committee. It was exciting to have research from different nutrition and dietetic practice areas represented. Thanks for all of the abstract submissions! Eight Early Bird abstracts were presented as Lightning Rounds during the virtual DC National Conference and were very well-received. The remaining 16 abstracts were displayed as posters for the duration of the conference with a live 7-min presentation opportunity from the poster gallery on May 19, 2021. All of the Early Bird abstracts are published in this issue of the Canadian Journal of Dietetic Practice and Research and are also featured on the CFDR website. These abstracts represent a wide variety of practice-based nutrition research projects in Canada. The Early Bird abstract research event would not have been possible without the commitment and dedication of many supportive individuals. On behalf of DC and CFDR, we extend a special thank you to members of our abstract review committee: Susan Campisi (University of Toronto); Pauline Darling (University of Ottawa); Andrea Glenn (St. Francis Xavier University); Mahsa Jessri (University of British Columbia); Grace Lee (University Health Network), Jessica Lieffers (University of Saskatchewan); Shelley Vanderhout (University of Toronto). A sincere thank you to all of the moderators, the DC Conference team and Izabella Bachmanek for their support with the Lightning Round presentations over the course of the DC virtual conference. Please consider submitting an abstract next year for the CFDR Research Showcase at the 2022 DC National Conference in Saskatoon, SK. Wishing all of you a wonderful Fall! Warm regards, Christina Lengyel PhD RD Chair, 2021 Early Bird Committee Professor Director of the Dietetics Program Food and Human Nutritional Sciences University of Manitoba Janis Randall Simpson PhD RD FDC FCNS Executive Director, CFDR Professor Emerita University of Guelph This article was corrected after publication.
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 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.276 | 0.135 |
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".