CFDR Research Showcase, Early Bird Abstracts: June 6, 2019
Bibliographic record
Abstract
Through the support of Dietitians of Canada (DC) and the Canadian Foundation for Dietetic Research (CFDR), the 2019 Research Showcase at the DC conference represented a wide variety of practice-based nutrition research projects in Canada. The 2019 Research Showcase consisted of several new elements this year. Early Bird abstracts were considered for either oral or oral ePoster sessions. New this year was the presentation of 11 Early Bird abstracts as oral presentations within the regular conference sessions: we called these “Lightning Rounds”. The remaining 27 Early Bird abstracts were presented as oral ePosters on June 6, 2019; authors presented their research in 2–3 minutes plus 2 minutes for questions, using their ePosters. For 2019, there were 3 concurrent oral ePoster sessions. All accepted, and presented, abstracts from the Early Bird submission are published in the Journal. This research event would not be possible without the commitment and dedication of many people. On behalf of DC and CFDR, we extend a special thank you to members of our abstract review committee: Susan Campisi (University of Toronto); Elaine Cawadias (Retired); Andrea Glenn (St. Francis Xavier University); Brenda Hartman (Brescia College, Western University) Mahsa Jessri (University of Ottawa); and Jessica Lieffers (University of Alberta). Thanks also to our moderators for the Oral ePosters: Barb Anderson (Acadian University); Jane Bellman (Dietitians of Canada); Brenda Hartman (Brescia College, Western University); Jessica Lieffers (University of Alberta); Dawna Royall (Dietitians of Canada); and Lee Rysdale (Northern Ontario School of Medicine). We enjoyed interacting with many of you at the oral research presentations as we highlighted the findings from our dietetic colleagues across our country! Finally, a special thank you to Gareth Willows Tribe and Michelle Naraine at CFDR for their assistance and support throughout the review process. Christina Lengyel, PhD, RD Chair, 2019 Early Bird Committee Associate Professor Director of the Dietetics Program Human Nutritional Sciences University of Manitoba Janis Randall Simpson, PhD, RD, FDC Chair, 2019 Late Breaking Committee Professor Emerita University of Guelph Executive Director, 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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.583 | 0.325 |
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".