Testing a novel isokinetic dynamometer constructed using a 1080 Quantum
Bibliographic record
Abstract
This study sought to assess the reliability and comparability of two custom-built isokinetic dynamometers (Model A and Model B) with the gold-standard (Humac Norm). The two custom-built dynamometers consisted of commercially available leg extension machines attached to a robotically controlled resistance device (1080 Quantum), able to measure power, force and velocity outputs. Twenty subjects (14m/6f, 26±4.8yr, 176±7cm, 74.4±12.4kg) performed concentric leg extensions on the custom-built dynamometers and the Humac Norm. Fifteen maximal leg extensions were performed with each leg at 180° s-1, or the linear equivalent (~0.5m s-1). Peak power (W), mean power (W), and fatigue indexes (%) achieved on all three devices were compared. Both custom-built dynamometers revealed high reliability for peak and mean power on repeated tests (ICC>0.88). Coefficient of variation (CV) and standard error of measurement (SEM) were small when comparing power outputs obtained using Model A and the Humac Norm ([Formula: see text] CV = 9.0%, [Formula: see text] SEM = 49W; peak CV = 8.4%, peak SEM = 49W). Whereas, Model B had greater variance ([Formula: see text] CV = 13.3% [Formula: see text] SEM = 120W; peak CV = 14.7%, peak SEM = 146W). The custom-built dynamometers are capable of highly reliable measures, but absolute power outputs varied depending on the leg extension model. Consistent use of a single model offers reliable results for tracking muscular performance over time or testing an intervention.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".