Demandas cinemáticas de competición internacional en el hockey sobre hierba femenino.
Bibliographic record
Abstract
Objective. To compare the kinematic demands on international \nwomen field hockey players during official competition \nmatches. Materials and methods. Sixteen women players \n(age: 24.7 ± 2.8; weight: 57.9 ± 5.9 kg; height: 165.2 ± 4.9 \ncm) belonging to the Spanish national team were monitored \nduring 5 matches of the European Championship using global \npositioning systems (GPS). The analyses were carried out \naccording to the players’ positions (defenders, midfielders \nand forwards), the quarters in the game (Q1, Q2, Q3, Q4), \nand the number of minutes played. The data analysed included \ndistances, accelerations and decelerations in different \nintensity ranges. Results. The defenders showed less high-intensity \nactivity (speeds, accelerations and decelerations) \nthan midfielders and forwards (9.4 ± 2.4%; ES: 0.78 with \nthe midfielders and 33.1 ± 7.2%; ES: 2.1, with the defenders). \nThe analysis by quarters showed that in Q4 activity \nwas the highest for all positions. In terms of the number of \nminutes played, the cluster analysis grouped the players into \n3 groups according to the number of minutes played (<32, \n32-45 and >45 minutes). The athletes who played <32 covered \nthe greatest distance at a sprint (>21 km/h) and high-intensity \ndistance (>15 km/h) per minute of play compared \nto the group who played >45 minutes. Conclusions. The \nresults of this study show that the physical demands on élite \nwomen hockey players depend on their position on the field, \nand that there is more activity in the last quarter and less \nrelative high-intensity kinematic activity among the players \nwho play more minutes during the match.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".