The Human Gene Map for Performance and Health-Related Fitness Phenotypes: The 2003 Update
Bibliographic record
Abstract
RANKINEN, T., L. PÉRUSSE, R. RAURAMAA, M. A. RIVERA, B. WOLFARTH, and C. BOUCHARD. The Human Gene Map for Performance and Health-Related Fitness Phenotypes: The 2003 Update. Med. Sci. Sports Exerc., Vol. 36, No. 9, pp. 1451–1469, 2004. This review presents the 2003 update of the human gene map for physical performance and health-related fitness phenotypes. It is based on peer-reviewed papers published by the end of 2003 and includes association studies with candidate genes, genome-wide scans with polymorphic markers, and single-gene defects causing exercise intolerance to variable degrees. The genes and markers with evidence of association or linkage with a performance or fitness phenotype in sedentary or active people, in adaptation to acute exercise, or for training-induced changes are positioned on the genetic map of all autosomes and the X chromosome. Negative studies are reviewed but a gene or locus must be supported by at least one positive study before being inserted on the map. By the end of 2000, 29 loci were depicted on the first edition of the map. In contrast, the 2003 human gene map for physical performance and health-related phenotypes includes 109 autosomal gene entries and QTL, plus two on the X chromosome. Moreover, there are 15 mitochondrial genes in which sequence variants have been shown to influence relevant fitness and performance phenotypes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".