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Record W2278966667

Estágio académico: Felner Tennis Academy

2015· report· pt· W2278966667 on OpenAlexaboutno aff

Bibliographic record

VenuePortuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · 2015
Typereport
Languagept
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science
DOInot available

Abstract

fetched live from OpenAlex

Resumo O presente relatório surge no âmbito do Estágio Curricular do Mestrado em Psicologia do Desporto e do Exercício, da Escola Superior de Desporto de Rio Maior, realizado entre o mês de Setembro de 2013 e o mês de Junho de 2014, no Felner Tennis Academy. O meu grupo de atletas é constituído por quatro atletas de ambos os sexos, de alto rendimento, no que diz respeito ao Treino Mental e um grupo de atletas, de ambos os sexos, com idades compreendidas entre os 8 e os 14 anos para o trabalho de Dinâmicas de Grupo. O objetivo geral de estágio foi potenciar o desenvolvimento de capacidades psicológicas no atletas e noutros agentes desportivos, que permitam por um lado minorar a influência de fatores externos e acima de tudo otimizar o rendimento, tanto ao nível do treino como da competição, como refere Dosil (2008). O relatório estrutura-se em três partes: a intervenção no ténis, onde foi feita uma avaliação quantitativa e qualitativa inicial e final, focadas na observação, entrevista e questionário “The Ottawa Mental Skills Assessment Tool” OMSAT-3R (Durand-Bush, Salmela, e Green-Demers, 2001; traduzido, adaptado e em processo de validação por Carlos Silva, Carla Borrego e António Rosado), foram ainda implementados treinos de competências psicológicas para a otimização do rendimento desportivo dos atletas e um trabalho de dinâmica de grupos.

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)0.005
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.070
Threshold uncertainty score0.139

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.006
Science and technology studies0.0060.002
Scholarly communication0.0070.003
Open science0.0010.005
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0280.003

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 head0.183
GPT teacher head0.476
Teacher spread0.293 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2015
Admission routes1
Has abstractyes

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