Modeling the Early and Late Acceleration Phases of the Sprint Start in Elite Long Track Speed Skaters
Bibliographic record
Abstract
ABSTRACT: Zukowski, MH, Jordan, MJ, and Herzog, W. Modeling the early and late cceleration phases of the sprint start in elite long track speed skaters. J Strength Cond Res 38(2): 236-244, 2024-This study established the reliability of an exponential function to model the change in velocity during the speed skating sprint start and the validity of associated model parameters in a group of subelite and elite long track speed skaters. Long track speed skaters ( n = 38) performed maximal effort 50-m on-ice accelerations from a standing start while tethered to a horizontal robotic resistance device that sampled position and time data continuously. An exponential function was applied to the raw data to model the change in velocity throughout the acceleration phase and compute the maximal skating speed (MSS), maximal acceleration capacity (MAC), maximum relative net horizontal power ( PMax ), and an acceleration-time constant ( τ ). All constructed models provided a sufficient fit of the raw data ( R -squared > 0.95, mean bias <2%). Intraday reliability of all model parameters ranged from good to excellent (intraclass correlation coefficient >0.8 and coefficient of variation <5%). Strong negative correlations ( r : -0.72 to -0.96) were observed between MSS and PMax and the 10 and 20 m split times measured with the robotic resistance and with 100 split times obtained from 500 m races. Moderate-to-large between-group differences were observed in MSS, MAC, and PMax between the elite vs. subelite speed skaters (Cohen d effect sizes: 1.18-3.53). Our results indicate that monoexponential modeling is a valid and reliable method of monitoring initial acceleration performance in elite level long track speed skaters.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".