Additional file 1 of A Systematic Review and Meta-analysis of the Association Between ACTN3 R577X Genotypes and Performance in Endurance Versus Power Athletes and Non-athletes
Bibliographic record
Abstract
Additional file 1: The funnel plots for the comparison of genotype frequencies: Figure S1. Funnel plot of ACTN3 R577X polymorphism in power athletes (RR vs. RX genotypes). Figure S2. Funnel plot of ACTN3 R577X polymorphism in power athletes (RR vs. XX genotypes). Figure S3. Funnel plot of ACTN3 R577X polymorphism in power athletes (RX vs. XX genotypes). Figure S4. Funnel plot of ACTN3 R577X polymorphism in power athletes (R vs. X alleles). Figure S5. Funnel plot of RR genotype expression in power athletes versus controls. Figure S6. Funnel plot of RX genotype expression in power athletes versus controls. Figure S7. Funnel plot of XX genotype expression in power athletes versus controls. Figure S8. Funnel plot of R allele in power athletes versus controls. Figure S9. Funnel plot of X allele in power athletes versus controls. Figure S10. Funnel plot of RR genotype expression in power versus endurance athletes. Figure S11. Funnel plot of RX genotype expression in power versus endurance athletes. Figure S12. Funnel plot of XX genotype expression in power versus endurance athletes. Figure S13. Funnel plot of R allele in power athletes versus endurance athletes. Figure S14. Funnel plot of X allele in power athletes versus endurance athletes.
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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 | 0.005 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.763 | 0.027 |
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".