Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Genes control biological processes such as muscle production of energy, mitochondria biogenesis, bone formation, erythropoiesis, angiogenesis, vasodilation, neurogenesis, etc. DNA profiling for athletes reveals genetic variations that may be associated with endurance ability, muscle performance and power exercise, tendon susceptibility to injuries and psychological aptitude. Already, over 200 genes relating to physical performance have been identified by several research groups. Athletes' genotyping is developing as a tool for the formulation of personalized training and nutritional programmes to optimize sport training as well as for the prediction of exercise-related injuries. On the other hand, development of molecular technology and gene therapy creates a risk of non-therapeutic use of cells, genes and genetic elements to improve athletic performance. Therefore, the World Anti-Doping Agency decided to include prohibition of gene doping within their World Anti-Doping Code in 2003. In this review article, we will provide a current overview of genes for use in athletes' genotyping and gene doping possibilities, including their development and detection techniques.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it