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Record W2157722132 · doi:10.1123/pes.12.3.300

Evaluation of the Canadian Aerobic Fitness Test with 10- to 15-Year-Old Children

2000· article· en· W2157722132 on OpenAlexaboutno aff
Anne Garcia, Jennifer S. Zakrajsek

Bibliographic record

VenuePediatric Exercise Science · 2000
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsnot available
FundersUniversity of Michigan
KeywordsAnthropometryVO2 maxTreadmillAerobic exerciseTest (biology)Physical fitnessCardiovascular fitnessFitness testPhysical therapyMedicineRegression analysisStep testDemographyStatisticsMathematicsSignificant differenceHeart rateInternal medicineBlood pressureBiologyEcology

Abstract

fetched live from OpenAlex

The objective of this study was to evaluate the utility of the Canadian Aerobic Fitness Test (CAFT), a field measure of cardiovascular fitness. After providing anthropometric measures, 31 subjects, ages 10 to 15, completed a maximal treadmill test and the CAFT, a 3-stage step test. Multiple regression analyses were conducted where maximal oxygen consumption from the treadmill test was estimated based on the oxygen cost of stepping, age and various combinations of body composition. For the total sample, the best model (R = 0.79, SEE = 6.7), obtained from the sum of 4 skinfolds, was the body composition estimate. This model was slightly more accurate for males (R = 0.83, SEE = 6.0) than for females (R = 0.77, SEE = 7.0). When the regression equation incorporated less time consuming indicators of body composition, the predictive power, albeit lower, was still satisfactory. It appears that the CAFT can be a useful option for measuring cardiovascular fitness for youth, with the decision dependent on the purpose of the test, the testing resources, the setting, and the motivation of the subjects.

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.342
Threshold uncertainty score0.680

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.247
Teacher spread0.237 · 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 designObservational
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

Citations14
Published2000
Admission routes1
Has abstractyes

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