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Record W2984116910 · doi:10.1139/apnm-2019-0616

Bias varies for bioimpedance analysis and skinfold technique when stratifying collegiate male athletes’ fat-free mass hydration levels

2019· article· en· W2984116910 on OpenAlexvenueno aff
Brett S. Nickerson, Ronald L. Snarr, Greg A. Ryan

Bibliographic record

VenueApplied Physiology Nutrition and Metabolism · 2019
Typearticle
Languageen
FieldMedicine
TopicBody Composition Measurement Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsFat free massSkinfold thicknessAthletesFat massLimits of agreementBioelectrical impedance analysisAnimal scienceChemistryMedicineBody mass indexInternal medicinePhysical therapyBiologyNuclear medicine

Abstract

fetched live from OpenAlex

This study evaluated the accuracy of bioimpedance analysis (BIA)- and skinfold (SF)-based body fat percentage estimates in collegiate athletes with varying fat-free mass (FFM) hydration levels. Subjects were evaluated as a whole (n = 63) and at FFM hydration levels of 64.00%–68.99% (FFM-Hydration L1 ; n = 37) and 69.00%–74.00% (FFM-Hydration L2 ; n = 26). Proportional bias was absent in the SF technique when stratifying FFM hydration levels. Contrarily, proportional bias was observed when using BIA for FFM-Hydration L1 , but not in FFM-Hydration L2 . Novelty Fat-free mass hydration levels impact BIA-based body fat estimates more than skinfold-based body fat.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.210
Threshold uncertainty score0.903

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.034
GPT teacher head0.272
Teacher spread0.238 · 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 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".

Quick stats

Citations5
Published2019
Admission routes1
Has abstractyes

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