Galeno entrenador. Una lectura de De Sanitate Tuenda = Galen, the coach. A Reading of De Sanitate Tuenda
Bibliographic record
Abstract
ResumenA Galeno se le conoce principalmente por sus críticas hacia el deporte profesional. Sin embargo, el presente artículo demuestra que era muy buen conocedor de las prácticas deportivas cotidianas y de los ejercicios practicados para desarrollar la velocidad, la fuerza y la potencia. Se deriva de esto que debió de observar a los atletas en sus entrenamientos muy atentamente. En su tratado Sobre la Salud (De Sanitate Tuenda) Galeno también habla acerca de la necesidad del calentamiento y enfriamiento para evitar la fatiga, la importancia de individualizar el entrenamiento y los momentos adecuados para la práctica deportiva. La terminología utilizada por galeno ha llegado hasta nuestros días. AbstractGalen is usually known as a critic of professional sport. On the other hand, I contend in this article that he was very familiar with the techniques needed for developing the strength, power and speed of the athletes and the daily routines of exercise. He must have observed athletes during their training very carefully. In his treatise On Health (De Sanitate Tuenda) Galen describes the need for warming up and cooling down when exercising as a way to avoid fatigue. He mentions also the importance of adapting the training routines to individuals and training at the proper time. His terminology is very modern and has arrived to us.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.052 | 0.011 |
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".