Hoja de cálculo para la cuantificación del entrenamiento en piragüismo (Spreadsheet for training quantification in canoeing)
Bibliographic record
Abstract
El proceso de planificación y programación del entrenamiento deportivo es una de las actividades más difíciles que realizan los entrenadores, ya que conlleva una gran complejidad valorar la interacción de los diferentes tipos de cargas y contenidos de entrenamiento. Por otro lado, la cuantificación del entrenamiento realizado o planificado, en ocasiones, puede convertirse en una tarea aburrida y repetitiva, por lo que el uso de una herramienta informática de carácter genérico, como una hoja de cálculo, puede facilitar y ahorrar mucho tiempo al entrenador en este tipo de actividades, además de servir para obtener información de forma instantánea de lo planificado a lo largo de la temporada. Por todo ello, el objetivo de este artículo fue aportar una hoja de cálculo sencilla, gratuita y práctica para la planificación y cuantificación del entrenamiento deportivo, en esta ocasión adaptada al piragüismo, pero adaptable a otros deportes.Abstract: The planning and programming process in sport training is one of the most difficult activities made by coaches, because analysing the interaction between different types of loads and task volume is a very complex activity. On the other hand, the quantification of the training volumes can sometimes be boring and repetitive. In this case, the use of computer tools, like spreadsheets, can help coaches by saving a lot of time in these tasks. Moreover, the data plan is gathered throughout the season and displayed in real time. The aim of this paper is to contribute with a free, easy and useful spreadsheet for the planning and programming of sport training, in this case related to canoeing, but also useful for others sports.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".