Aplicación del Diagnóstico Morfofuncional de la Población Estudiantil comprendida en dos Planteles Educativos en edades de 6 a 12 años en las Escuelas Teodoro Wolf y Escuela N°13 Ballenita del Cantón Santa Elena de la Provincia de Santa Elena.
Bibliographic record
Abstract
Numerous studies have emerged related to the Kineanthropometry since its inception as a science, with a body of doctrine itself, at the International Congress of Physical Activity in Montreal in 1976 (Chamorro, 1993).As we pointed out the International Society for the Advancement of Kinanthropometry (ISAK, 2001), anthropometric studies include a number of different computation tools for data analysis, such as somatotype, fractionation of body mass estimates proportionality or prediction of body density with various regression equations. From this standpoint, Norton etal.(2004) note that within the wide range of factors that influence athletic performance, anthropometric measurements in an athlete are variables that can play an important role in determining the potential success in certain sports. These same authors indicate the need to study the morphological profiles of the best athletes in each specialty. Hence the aim of this study is to obtain a baseline morphological profile, focusing on the somatotype and body composition of the student population of eight schools in the Province of Santa Elena, aged from 11 to 15 years
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| 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) | 0.001 | 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".