Influence of sex and specialty on the prediction of middle-distance running performances using the Mercier et al.’s nomogram
Bibliographic record
Abstract
The aim was to test the influence of sex and specialty (3000, 5000 and 10000 m) on the validity of middle-distance running performance predictions obtained from the Mercier et al.'s nomogram. Consequently, we examined all official French track running rankings for the 3000-, 5000- and 10000-m events (men and women) from 2006 to 2012. A scoring table was used to determine the runners' specialties. Only runners who performed in the three distance events within the same year were included (75 women and 400 men). The Mercier et al.'s nomogram was used to predict one running performance from the other two. The results showed no significant difference between actual and predicted running performances (P = 0.77). Female runners had significantly lower performances than male runners (P < 0.001). Specialty did not influence performances (P = 0.11). Very high correlations were found between actual and predicted performances (0.91 < r < 0.99), with the exception of women (r = 0.85) in 5000 m. Moreover, low limits of agreement were obtained for male and female runners, whatever the specialty. These results support the validity of the nomogram to predict running performance in the 3000-, 5000- and 10000-m events for male and female runners, whatever the specialty. The predicted running performances may be used in training programmes (e.g., to prescribe tempo runs) and competitions (e.g., to establish split times).
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".