THE EFFECT OF TRUNK AND LEG LENGTH ON SIT AND REACH SCORES
Bibliographic record
Abstract
Flexibility is one component of physical fitness. The fitness industry and school have continually used the Well's sit and reach test to assess flexibility. There has been divergent results as to the influence of one's segment lengths on the sit and reach (SR) scores. This aim of this study was to examine the effects of segment lengths on SR scores in boys over a 7-year period. 181 boys participated in the Saskatchewan Growth Study were measured annually for standing height, sitting height, leg length and SR, from age 10 to 16 years. Longitudinal standing height data was used to determine peak height velocity (PHV) and maturational ages. The mean SR score at chronological ages ranged from 19.86 to 22.59 cm. Adjusting for maturation, SR scores ranged from 19.67 to 24.36cm. Prior to PHV, both chronological and maturational measurements showed there was an increase in leg length, followed by an acceleration of the trunk segment. Only at a chronological age of 12 years was there a significant correlation between SR and leg length, r = −0.156 (p < 0.05). Maturation age showed a significant correlation of 60.237, −0.212, and 60.174 between height and SR scores at 4, 3 and 2 years prior to PHV, p < 0.05. Sitting height significantly influenced SR 3 years prior to PHV, −0.211 (p < 0.05). Leg length was negatively associated with SR at 5, 4, 3, and 2 years prior to PHV, −0.264, −0.239, −0.182 and 60.175 respectively. Maturational data provided evidence to show a decrease in flexibility prior to PHV to be related to an increase in leg length. Chronological data failed to detect any significant relationship. This data indicates segment lengths only significantly influence SR score from 2–4 year prior to PHV in boys.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".