Abstracts from Dietetic Research Event: June 09–11, 2016
Bibliographic record
Abstract
Winnipeg, Manitoba was the host city of the 2016 Dietitians of Canada Annual Conference. Through the support of Dietitians of Canada and CFDR, the 2016 event was both an exciting and informative exchange of research and experience-sharing efforts that inspired attendees. The submissions for this year's Canadian Foundation for Dietetic Research (CFDR) event represented the diversity of dietetic research conducted within Canada. The topics highlighted from this year's abstracts include Community Based Nutritional Care, Wellness & Public Health, Determinants of Food Choice, Dietary Intake, Nutrition Health & Education, Dietetic Practice & Education, Clinical Research & Patient Service, and Nutrition Social Media & the Web. Each presenter provided an 11-minute oral presentation (8 minutes for presenting and 3 minutes for questions). This allowed for meaningful interaction between the presenters and those attending the sessions. This year there were professional and student oral research presentations on each day of the conference. These presentations offered the newest insights into important research findings that apply to dietetic practice. This research event would not be possible without the commitment and dedication of many people. On behalf of Dietitians of Canada and CFDR, I would like to extend a special thank you to the 2016 Abstract Review Committee who represented research, clinical nutrition, community nutrition, and education: Masha Jessri (Ph.D Candidate, University of Toronto), Joyce Slater (Associate Professor, University of Manitoba) and Miyoung Suh (Associate Professor, University of Manitoba). We would also like to thank all of our moderators who assisted during the conference to keep our research presentation sessions on time: Marcia Cooper, Miyoung Suh, Andrea Buchholz, Dawna Royall, Paul Fieldhouse, Joyce Slater, Isabelle Giroux, and Bethany Hopkins. Finally, a special thank you to Michelle Naraine and Greg Sarney at CFDR for their assistance and support throughout the review process. I enjoyed interacting with many of you at the oral research presentations as we highlighted the findings from our dietetic colleagues across our country! Christina Lengyel, PhD, RD Chair, 2016 Abstracts Review Committee Associate Professor Director of the Dietetics Program Human Nutritional Sciences University of Manitoba.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.443 | 0.250 |
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".