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Sports or/and Special Populations: Training Physiology in Health and Sports Performance

2023· book· en· W4388420534 on OpenAlexfundno aff

Bibliographic record

Venuenot available
Typebook
Languageen
FieldMedicine
TopicCardiovascular Effects of Exercise
Canadian institutionsnot available
FundersArmy Research Institute for the Behavioral and Social SciencesNatural Sciences and Engineering Research Council of CanadaMinistry of Science and ICT, South KoreaKorea Nazarene UniversityMinistry of EducationUniversidade da Beira InteriorNational Research FoundationStyrelsen för Internationellt UtvecklingssamarbeteNational Research Foundation of KoreaUniversidade Federal de GoiásUniversidade de Trás-os-Montes e Alto DouroInternational Development Research Centre
KeywordsTraining (meteorology)Special populationsSports medicinePsychologyPhysiologyMedicinePhysical therapyGeography

Abstract

fetched live from OpenAlex

Several factors have been identified as interfering with the success, rehabilitation, and fitness of athletes from childhood to adulthood, as well as in para-sport, and special populations, according to research. The performance and health of this population are affected by the relationships between stress, maturation, training load, and recovery. Environmental approaches aim to increase efficiency and physiological adaptations in this sense. In various situations and conditions, however, this stimulusperformanceadaptation relationship varies.As a result, we received contributions related (but not limited) to the following topics: training load monitoring; stress and physiological responses during exercise or sports; recovery process after exercise; changes after stress and/or training load; physiology of training in health and sports performance.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.907
Threshold uncertainty score0.963

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.037
GPT teacher head0.297
Teacher spread0.260 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2023
Admission routes1
Has abstractyes

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