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DETERMINANTES DO TEMPO MÉDIO DE SUBSTITUIÇÕES NO FUTEBOL: UMA ANÁLISE DO CAMPEONATO BRASILEIRO DE 2014

2019· article· pt· W2969107632 on OpenAlexaff
Cláudio Djissey Shikida, Ari Francisco Araujo Júnior, Rafael Canuto Neves

Bibliographic record

VenueSINERGIA - Revista do Instituto de Ciências Econômicas Administrativas e Contábeis · 2019
Typearticle
Languagept
FieldSocial Sciences
TopicPhysical Education and Sports Studies
Canadian institutionsAdidas (Canada)
Fundersnot available
KeywordsPhysics

Abstract

fetched live from OpenAlex

Este estudo analisa os padrões da primeira substituição de jogadores de cada time durante o intervalo ou o segundo tempo de jogos na primeira divisão do Campeonato Brasileiro de 2014. Foram usados modelos MQO (Mínimos Quadrados Ordinários) e Tobit, ambos com correção para heterocedasticidade. Os resultados mostram que times que estão ganhando tendem a fazer a primeira mudança nos momentos finais da partida. Consequentemente, substituições defensivas ocorrem antes das ofensivas. Também existem evidências de que a classificação no campeonato de ambas as equipes em campo tem influência no momento em que a alteração ocorre.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.759
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0020.002
Scholarly communication0.0030.001
Open science0.0020.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.024
GPT teacher head0.323
Teacher spread0.299 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations0
Published2019
Admission routes1
Has abstractyes

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