Bibliographic record
Abstract
The fourth edition of Clinical Sports Nutrition is written by Louise Burke, the head of the department of Sports Nutrition for the Australian Institute of Sport, and by Vicki Deakin, senior lecturer and head of Nutrition and Dietetics at the University of Canberra. Contributions have also been made by leading sports dieticians, physicians, and academics. The text is written for students interested in a career in sports nutrition, as well as professionals in sports nutrition and medicine. New additions to the fourth edition include chapters on exercise and the immune system, antioxidants and the athlete, food services for athletes and nutrition for travel. Updated position statements by the ACSM and IOC have been included. No information on nutrition and athletic injuries is provided. The text is written in language that is very readable for anyone with a background in the subject matter and does an excellent job of summarizing the raw science without burdening the reader with too much detail. Where further detail may be warranted, references are provided for the reader to guide them to appropriate literature to delve deeper into a given topic. The main strengths of this text are its readability and the wide range of topics within sports nutrition that it covers. Future editions should include information regarding nutrition for the injured athlete and more visually appealing images.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.170 | 0.105 |
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".