CFDR Research Showcase, Early Bird Abstracts
Bibliographic record
Abstract
Due to the cancellation of the Dietitians of Canada (DC) conference for 2020 because of the COVID-19 pandemic, the 2020 Research Showcase was not able to be held as planned in Saskatoon in June 2020. Ten of the Early Bird abstracts were presented as Lightning Rounds during the virtual DC conference that was held in June and July, 2020. All 25 Early Bird abstracts are published in this issue of the Canadian Journal of Dietetic Practice and Research and are also posted 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 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); Pauline Darling (University of Ottawa); Andrea Glenn (St. Francis Xavier University); Mahsa Jessri (University of British Columbia); Jessica Lieffers (University of Saskatchewan); Shelley Vanderhout (University of Toronto). Thanks also to the moderators for the Lightning Round presentations in the virtual DC conference. Finally, thanks also to the DC Conference team for their support with the Lightning Round presentations over the course of the DC virtual conference. Christina Lengyel PhD RD Chair, 2020 Early Bird Committee Associate Professor Director of the Dietetics Program Human Nutritional Sciences University of Manitoba Janis Randall Simpson PhD RD FDC FCNS 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 | 0.011 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.376 | 0.145 |
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".