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Record W4411656587 · doi:10.51847/aibfyfjxkp

10.51847/AibFyFJXKP

2000· article· en· W4411656587 on OpenAlexvenueno aff

Bibliographic record

VenueTime to knit · 2000
Typearticle
Languageen
FieldEngineering
TopicDyeing and Modifying Textile Fibers
Canadian institutionsnot available
Fundersnot available
KeywordsAerobic exerciseNicotinePhysical therapyMedicinePhysical medicine and rehabilitationInternal medicine

Abstract

fetched live from OpenAlex

Objective: The aim of this study was to investigate the effect of aerobic exercise for 3 months on serum levels of CRP in low-activity smoker and non-smoker males.Materials and Methods: For this purpose, 26 adult male volunteers were chosen randomly to participate in the study in two groups: smokers (number = 13) and non-smokers (number = 13).Both research groups participated in a three-month aerobic exercise program of 3 sessions ranging from 45 to 60 minutes.Exercise intensity was considered 60 to 80 percent of the maximum heart rate during the exercise program.Independent t-test and t-correlated test were used to analyze the data.Results: findings showed that there is a significant difference between CRP levels, body weight, body mass index, and body fat percentage in adult male smokers and non-smokers.The results also indicated that 12 weeks of aerobic training and nicotine treatments improved serum CRP levels, body weight, body mass index, and body fat percentage in adult male smokers.Conclusion: The findings of the present study showed that inactive smoker males have higher levels of CRP as inflammatory cytokines in compare to non-smokers, which confirms a number of previous studies.On the other hand, the intervention of aerobic training for 12 weeks leads to a decrease in CRP in both smokers and non-smokers.However, the improvement in the smokers group is much more than the non-smokers group.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.138
Threshold uncertainty score0.197

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.8620.795

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 head0.005
GPT teacher head0.161
Teacher spread0.156 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

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
Published2000
Admission routes1
Has abstractyes

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