Core Concepts in Human Genetics: Understanding the Complex Phenotype of Sport Performance and Susceptibility to Sport Injury
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
High-throughput sequencing of multiple human exomes and genomes is rapidly identifying rare genetic variants that cause or contribute to disease. Microarray-based methodologies have also shed light onto the genes that contribute to common, non-disease human traits such as hair and eye colour. Sport scientists should keep in mind several things when interpreting the literature, and when designing their own genetic studies. First of all, most genetic association methods are more powerful for detecting disease phenotypes (such as susceptibility to injury) than they are for detecting healthy phenotypes (such as sport performance). This is because there are likely to be many more biological factors contributing to the latter, and the effect size of most of these biological factors is likely to be small. Second, implicating a particular gene in a human phenotype like athletic performance or injury susceptibility requires an unbiased population data set. Third, new types of non-coding biological variability continue to be uncovered in the human genome (e.g. epigenetic modifications, microRNAs, etc.). These other types of variability may contribute significantly to differences in athletic performance.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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