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Record W2768000516 · doi:10.19146/pibic-2017-79122

Reprodutibilidade do Rast Test para o Rugby em Cadeira de Rodas

2017· article· pt· W2768000516 on OpenAlexaff
Inaene dos Santos Magalhães, José Irineu Gorla, Luís Gustavo de Souza Pena

Bibliographic record

VenueAnais do Congresso de Iniciação Científica da Unicamp · 2017
Typearticle
Languagept
FieldMedicine
TopicSports Performance and Training
Canadian institutionsQUAD Engineering (Canada)
Fundersnot available
KeywordsTest (biology)Geology

Abstract

fetched live from OpenAlex

Criado na década de 70 por pessoas que não podiam praticar Basquete em Cadeira de Rodas por causa de um maior comprometimento motor (tetraplegia, sequela de poliomielite, quadri-amputados) o Rugby em Cadeira de rodas (RCR) vem ganhando destaque ao longos das competições.E assim como em muitos esportes coletivos a capacidade anaeróbia é muito importante para o desempenho dentro de quadra, a qual é caracterizada pela capacidade de regenerar ATP a partir de fontes não advindas das mitocôndrias.E um dos principais métodos de avaliação dessa capacidade, o RAST TEST (running anaerobic sprint test) ou, em português, teste de tiros em velocidade de corrida anaeróbica, que no caso do RCR, é realizado por meio de dez tiros de vinte metros com seis segundos de pausa entre cada estímulo (protocolo de vinte metros, adaptado).Portanto, o presente estudo visa a reprodutibilidade do Rast Test (Running Anaerobic Sprint Test) para o RCR com o time da ADEACAMP que treina na Faculdade de Educação Física da UNICAMP (FEF) pelo projeto de Extensão da própria faculdade.

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)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.057
GPT teacher head0.352
Teacher spread0.295 · 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 designBench or experimental
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".

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Citations0
Published2017
Admission routes1
Has abstractno

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