Can we quantify the benefits of “super spikes” in track running?
Bibliographic record
Abstract
The recent and rapid developments in track spike innovation have been followed by a wave of record-breaking times and top performances. This has led many to question what role ‘super spikes’ play in improving running performance. To date, the specific contributions of new innovations, including lightweight, resilient, and compliant midsole foam, altered geometry, and increased longitudinal bending stiffness, to track running performance are unknown. Based on current literature, we speculate about what mechanical advantages these features may provide. Importantly, the effects of super spikes will vary based on several factors including the event-specific speed (i.e. 100 m vs. 10,000 m) and the characteristics of the athlete wearing them. Further confounding our understanding of super spikes is the difficulty of testing them. Unlike marathon shoes, testing track spikes comes with a unique challenge of quantifying the metabolic energy demands of mid-distance running events, which are partly anaerobic. For this reason, putting an exact number on the benefits from super spikes is difficult.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".