Efecto general y residual de un programa de entrenamiento multicomponente para adultos mayores. Componentes de la condición física, carga interna y adherencia
Bibliographic record
Abstract
Comienzo dando estos agradecimientos a Dios, el ser supremo que guía mi fe y mi voluntad para ayudar al prójimo, quien, en los momentos de flaqueza y desesperación, con su infinita misericordia condujo mis pasos hacia el camino correcto.Enteramente agradecida con mis padres Rosa María Mena Nevárez y Carlos Medrano Peña, ellos a pesar de la distancia, siguen siendo el pilar fundamental para poder lograr las metas que me propongo, ellos siempre tuvieron las palabras correctas para aliviar mi cansancio y mejorar mi ánimo, todos mis éxitos se los debo y deberé a ellos.A mis hermanos Carlos Alberto y Verónica Medrano Mena, por ser ese motor que día a día me impulsó a demostrarles lo mejor de mí y que los sueños si se pueden cumplir.Gracias infinitas a mi abuelita Tomasa Nevárez Carrasco, mi inspiración para este gran logro.Mi segunda madre que, con su amor y cariño infinito a lo largo de todos estos años, me ha enseñado a ser respetuosa y servicial con el prójimo.Faltarían las hojas para poder describir la admiración y amor que siento por ella.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".