MétaCan
Menu
Back to cohort

Análisis del test de resistencia anaeróbica de 1000 metros en estudiantes varones de educación superior

2020· article· es· W3096442880 on OpenAlexvenueno aff
Orlando David Mazón Moreno, Víctor Hugo Herrera Mena, Jorge Giovanny Tocto Lobato, Juan Carlos Bayas Machado

Bibliographic record

VenueConcienciaDigital · 2020
Typearticle
Languagees
FieldHealth Professions
TopicHealth and Lifestyle Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

El objetivo de la investigación fue analizar los resultados obtenidos de la aplicación del test de resistencia anaeróbica de 1000 metros en estudiantes varones de las diferentes facultades de la Escuela Superior Politécnica de Chimborazo con edad media de 20 años, que cursan la asignatura de Educación Física en el período académico septiembre 2019 – febrero 2020. Para ello, se consideró una investigación de campo, descriptiva y temporal con una muestra intencional de 581 estudiantes varones, provenientes de diferentes regiones del Ecuador. Además de la edad y región, se registró por cada estudiante el peso, talla y el tiempo alcanzado en cubrir la distancia del test. Los datos recolectados fueron analizados estadísticamente para determinar cuartiles, los mismos que fueron etiquetados según una escala de Likert para posteriormente ser cuantificados. Se tabularon las frecuencias por cada cuartil y se procedió al análisis descriptivo mediante tablas y gráficos procesador en Microsoft Excel. De los datos procesados, se pudo concluir que la mayoría de los estudiantes (56%) alcanzaron tiempos de hasta 4,5 minutos, considerados como bueno y excelente en cubrir los 1000 metros de resistencia anaeróbica, donde la mayoría de los estudiantes son de la región sierra y que el 67% tienen un peso normal.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.038
GPT teacher head0.362
Teacher spread0.324 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueConcienciaDigitalSame topicHealth and Lifestyle StudiesFrench-language works237,207