A Brief Description of the Biomechanics and Physiology of a Strongman Event: The Tire Flip
Bibliographic record
Abstract
The purpose of this study was to (a) characterize the temporal aspects of a popular strongman event, the tire flip; (b) gain some insight into the temporal factors that could distinguish the slowest and fastest flips; and (c) obtain preliminary data on the physiological stress of this exercise. Five resistance-trained subjects with experience in performing the tire flip gave informed consent to participate in this study. Each subject performed 2 sets of 6 tire flips with a 232-kg tire with 3 minutes of rest between sets. Temporal variables were obtained from video cameras positioned 10 m from the tire, perpendicular to the intended direction of the tire flip. Using the "stopwatch" function in Silicon Coach, the duration of each tire flip and that of the first pull, second pull, transition, and push phases were recorded. Physiological stress was estimated via heart rate and finger-prick blood lactate response. Independent T-tests revealed that the 2 faster subjects (0.38 +/- 0.17 s) had significantly (p < 0.001) shorter second pull durations than the 3 slower subjects (1.49 +/- 0.92 s). Paired T-tests revealed that the duration of the second pull for each subject's fastest 3 trials (0.55 +/- 0.35 s) were significantly (p = 0.007) less than their 3 slowest trials (1.69 +/- 1.35 s). Relatively high heart rate (179 +/- 8 bpm) and blood lactate (10.4 +/- 1.3 mmol/L(-1)) values were found at the conclusion of the second set. Overall, the results of this study suggest that the duration of the second pull is a key determinant of tire flip performance and that this exercise provides relatively high degrees of physiological stress.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.044 | 0.011 |
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".