Do anthropometrics and functional measurements predict performance in the Sandbag Lift Task?
Bibliographic record
Abstract
Background: The Sandbag Lift Task (SLT) is a component of the Canadian Armed Forces (CAF) pre-employment fitness evaluation, which mimic demands of common military tasks. Understanding the relationship between anthropometrics, dynamic balance, core stability, and isometric strength may provide insight into SLT performance. Purpose: The purpose was to examine if anthropometrics such as BMI and limb lengths and circumferences, FMS trunk stability, anterior reach of YBT-LQ, isometric knee and hip extension strength, and core stability predict SLT performance. An exploratory purpose was to examine hip, knee, and ankle kinematics throughout SLT performance. Methods: Twenty-four participants (12 males; 12 females) completed two sessions 48-96 hours apart. Session one included height and weight measurements and SLT performance (mins/sec), which was recorded for kinematic analysis. Session two assessed predictors of limb lengths and circumferences (cm), trunk stability pushup (0-3), anterior reach asymmetry (cm), isometric knee and hip extension strength (N), and single leg wall sit hold (secs). Linear regression determined SLT variance explained by these predictors. Hip, knee, and ankle kinematics throughout SLT performance were derived from markerless motion capture. Results: For the total sample, single leg wall sit hold, leg length, and trunk stability pushup explained 55.1% (adjusted R2) of SLT variance. In males, single leg wall sit hold, shank circumference, and trunk stability pushup explained 48.1% (adjusted R2) of SLT variance. In females, thigh circumference, trunk stability pushup, shank circumference, isometric knee extension, shank length, and single leg wall sit hold explained 88.5% of SLT variance. Conclusion: These findings suggest development of targeted interventions to potentially optimize training for SLT performance with an additional focus on sex-specific interventions.
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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 | 0.000 | 0.002 |
| 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) | 0.004 | 0.001 |
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".