From Athens to Sparta—37 Years of Spartathlon
Bibliographic record
Abstract
(1) Background: Recent studies analyzed the participation and performance trends of historic races such as the oldest ultra-marathon (Comrades) or the oldest 100-km ultra-marathon (Biel). One of the toughest and historic ultra-marathons in the world is the ‘Spartathlon’ (246-km ultra-marathon from Athens to Sparta). The present study aimed to analyze the trends in participation and performance of this race. (2) Methods: Different general linear models were applied as follows: the first model was a two-way ANOVA (Decade × Sex), with separate models for all participants and for only the top five finishers in each race; the second model was a two-way ANOVA (Age Group × Sex); the third model was a two-way ANOVA (Nationality × Sex). (3) Results: Between 1982 and 2019, 3504 ultra-marathoners (3097 men and 407 women) officially finished the Spartathlon at least once. Athletes from Japan were the majority with 737 participants, followed by far by runners from Germany (n = 393), Greece (n = 326), and France (n = 274). The nations with the highest numbers of athletes amongst the top five performers were Japan (n = 71), followed by Germany (n = 59), and Great Britain (n = 31). Runners from the USA were the fastest in men, and runners from Great Britain were the fastest in women. Female and male runners improved performance across the decades. The annual five fastest women and men improved their performance over time. Runners achieved their best performance earlier in life (20–29 and 30–39 years) than female runners (30–39 and 40–49 years). Runners in age group 30–39 years were the fastest for all nationalities, except for Greece. (4) Conclusions: Successful finishers in the Spartathlon improved performance in the last four decades and male runners achieved their best performance ~10 years earlier in life than female runners.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".