Análisis del patrón de carrera sobre superficie artificial y natural en futbolistas adolescentes (Analysis of the running pattern on artificial and natural surface in adolescent football players)
Bibliographic record
Abstract
Introducción: Existe poca evidencia que detalle el comportamiento de cada variable espacio-temporal del patrón de carrera utilizando diferentes superficies. Objetivo: Comparar las variables espaciotemporales del patrón de carrera de futbolistas adolescentes en superficie natural y artificial. Método: se realizó un estudio de corte transversal con 18 jugadores de fútbol masculino (edad mediana= 12 años; Rango intercuartílico [RIC] 12-13). Mediante un sistema de medición óptico de 5 metros de longitud se analizó velocidad, aceleración, tiempo de contacto (Tc), tiempo de vuelo (Tv), fase de contacto, fase de apoyo, propulsión, zancada y cadencia. Las valoraciones fueron inicialmente desarrolladas en Superficie Artificial (SA) y 24 horas después en Superficie Natural (SN). Se utilizaron las pruebas Rangos con signos de Wilcoxon para datos pareados y el coeficiente de correlación de Spearman. Resultados: La SA mostró una fase de apoyo fue superior a la SN (SN: Me=0,05 RIC:0,03; 0,06; SA: Me=0,09 RIC 0,08;0,10; p <0,001). El Tv (SN: Me=0,16 RIC:0,14;0,19; SA: Me=0,04 RIC: 0,04;0,05; p<0,001), la fase de contacto (SN: Me=0,02 RIC:0,02;0,03; SA: Me=0,02 RIC: 0,01;0,02; p=0,040) y la propulsión (SN: Me=0,14 RIC:0,09;0,17; SA: Me=0,07 RIC:0,06;0,09; p=<0,001) fueron mayores en SN que en SA. Se encontró una relación indirecta entre velocidad y fase de contacto en SN. El Tv y la zancada se asociaron indirectamente con la aceleración en SA. Conclusión: el patrón de carrera varía según la superficie utilizada. La fase de contacto puede explicar la velocidad en la SN; mientras que el Tv y la zancada pueden explicar la aceleración en la SA.Abstract. Introduction: There is little evidence that details the behavior of each spatial-temporal variable of the running pattern using different surfaces. Objective: To compare the spatial-temporal variables of the running pattern over two surfaces in adolescent soccer players. Method: A cross-sectional study involving 18 male soccer players was conducted (median [Me] age = 12 years; Interquartile range [IQR] 12-13). Speed, acceleration, contact time (Ct), flight time (Ft), contact phase, support phase, propulsion, stride, and cadence were evaluated through a 5-meter long optical measurement system. The assessments were initially carried out on Artificial Surface (AS) and, 24 hours later, on Natural Surface (NS). The Wilcoxon signed-rank test for paired data and the Spearman correlation coefficient were used. Results: The support phase was greater in AS than NS (NS: Me = 0.05 IQR: 0.03; 0.06; AS: Me = 0.09 IQR 0.08; 0.10; p <0.001). The Ft (NS: Me = 0.16 IQR: 0.14; 0.19; AS: Me = 0.04 IQR: 0.04; 0.05; p <0.001), the contact phase (NS: Me = 0.02 IQR: 0.02; 0.03; AS: Me = 0.02 IQR: 0.01; 0.02; p = 0.040) and propulsion (NS: Me = 0.14 IQR: 0.09; 0.17; AS: Me = 0.07 IQR: 0.06; 0.09; p = <0.001) were greater in NS than AS. An indirect relationship between speed and contact phase in NS was found. The Ft and the stride were indirectly associated with acceleration in AS. Conclusion: The running pattern varies according to the surface used. The contact phase can explain the speed in the NS; while the Ft and the stride can explain the acceleration in AS.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".