Bibliographic record
Abstract
Nutrition Support for Athletes T he Ryley-Jeffs Memorial lecture given by Kelly Anne Erdman at Dietitians of Canada's 2015 conference titled A Lifetime Pursuit of a Sport Nutrition Practice and profiled in this issue, gives us a "behind the scenes" look at the world of sports nutrition.After the establishment of the National Sport Centre Calgary, Kelly Anne practiced as the first consulting dietitian for several Canadian national teams, including Hockey Canada.Career highlights have included the opportunity to work at 3 Games for the Canadian Olympic Committee as the Performance Dietitian for Team Canada athletes (2011 Pan American Games, 2012 London Summer Olympics, and the 2014 Sochi Winter Olympics).This involved ensuring that the Canadian athletes had ready access to safe and effective foods to perform at their best.Kelly Anne describes ongoing challenges in sport dietetics, including dealing with advice given to athletes by pseudo-nutritionists, and also the tremendous opportunities available for aspiring Canadian sport dietitians.As an honoured Ryley-Jeffs award recipient, Kelly Anne's visionary leadership and pioneering spirit in the field of sport nutrition, certainly exemplifies the vision of Violet Ryley and Kathleen Jeffs.Congratulations Kelly Anne! Check the current issue
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.018 | 0.016 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
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".