Precision in the Prediction of Middle Distance-Running Performances Using Either a Nomogram or the Modeling of the Distance-Time Relationship
Bibliographic record
Abstract
The purpose was to determine the levels of precision in the prediction of middle-distance performances in running using the modeling of the distance-time relationship and a nomogram. Official French running rankings for the men's 3,000; 5,000; and 10,000 m were scrutinized from 1996 to 2007. Only runners who competed over the 3 distances within the same year were included (n = 100). The distance-time relationship was modeled using a linear 2-parameter model from the plot of 2 performances to predict a third one. The nomogram of Mercier was also used to predict 1 performance from the use of the other 2. Actual and predicted performances were significantly different, except for the 5,000- and 10,000-m performances predicted from the nomogram (p > 0.05). Effect sizes (ESs) were lower when the performance was predicted by the nomogram (-0.25 < ES < 0.05) compared with the linear 2-parameter model (-0.99 < ES < 0.47). The predicted performances were significantly correlated to the actual performances (r > 0.46; p < 0.01). The bias ± limits of agreement for the 3,000-; 5,000-; and 10,000-m performances were 1.0 ± 12.8, -0.1 ± 6.9, and 0.1 ± 20.8% and 3.7 ± 15.5, -1.4 ± 6.2, and 2.5 ± 10.6% for prediction from the nomogram and distance-time relationship, respectively. Although the modeling of the distance-time relationship does not enable middle-running performances to be accurately predicted, the precision in the predictions from the nomogram suggests that the nomogram may be used to prescribe adapted training intensities and determine the optimal strategy during the race.
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.
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 | 0.032 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".