Field Tests and Kinematic Measures Predict Winter Performance in Junior Cross-Country Skiers
Bibliographic record
Abstract
Cross-country (XC) skiing performance relies on maximal aerobic capacity, technical skills, strength, and power of sport-specific muscle groups. Previous studies have shown that roller-skiing and running tests accurately predict XC skiing performance. However, there is a lack of research on highly trained junior skiers, particularly regarding the relationship between maximum speed, muscular endurance, and kinematic parameters in summer field tests and XC skiing performance. PURPOSE: To develop predictive equations for cross-country skiing performance in junior skiers based on results from field tests and kinematics. METHODS: Thirty-two junior skiers (16.1 ± 1.3 years old, 12 females) completed various tests, including the Canadian strength test (CST), 3000 m running, Ten-step bounding, and three roller-skiing tests. Technical parameters, such as cycle velocity (CV), cycle length (CL), cycle rate (CR), and others, were recorded during uphill double pole test. The relationships between overall time (OT) and rank (OR) in two XC skiing races were analyzed using Pearson correlation and predictive equations were developed through multiple-linear regression analysis. RESULTS: Strong correlations were found between OT and uphill double pole test (r = 0.85, p < 0.001), 50 m double pole (r = 0.82, p < 0.001), CST (r = 0.73, p < 0.001), and others. For OR, significant relationships were noted for each gender. Additionally, relationships between upper- and lower-body muscular endurance and kinematic parameters were established. Predictive equations based on field tests explained 73-89% of total variance in OT, while kinematic equations explained 57-60% (Table 1). CONCLUSION: Winter performance of junior skiers can be effectively predicted using sport-specific fitness and technical parameters measured during the preparatory summer period. Table 1. - Standardized Equations to Predict Overall Time and Rank with Field Tests and Kinematics in Junior Skiers. Model Standardized Equations R 2 1 Z OT = 0.85*Z UDPT 0.73 2 Z OT = 0.56*Z UDPT + 0.46*Z DS50 0.85 3 Z OR = 0.52*Z UDPT + 0.32*Z DS50 +0.96*Z G 0.76 4, Men Z OT = -0.45*Z SS + 0.60*Z DS50 0.83 5, Men Z OT = 0.91*Z UDPT 0.83 6, Men Z OT = 0.76 *Z UDPT + 0.29*Z DS50 0.89 7, Men Z OT = 0.92*Z DP50 0.84 8, Women Z OT = 0.83*Z DS50 0.69 9, Women Z OT = 0.43 *Z UDPT + 0.62*Z DS50 0.83 10, Women Z OT = 0.60*Z DS50 - 0.45 *Z SS 0.85 11 Z OT = -0.75*Z MCL 0.56 12 Z OT = -0.76*Z MCL 0.57 13 Z OT = -0.63*Z CL - 0.55*Z CR 0.60 14 Z OT = -0.54*Z CL + 0.52*Z PT 0.58
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".