A Framework to Guide Practitioners for Selecting Metrics During the Countermovement and Drop Jump Tests
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Bibliographic record
Abstract
ABSTRACT Researchers and practitioners have highlighted the necessity to monitor jump strategy metrics and the commonly reported outcome measures during the countermovement jump (CMJ) and drop jump (DJ) tests. However, there is a risk of confusion for practitioners, given the vast range of metrics that now seem to be on offer via analysis software when collecting data from force platforms. As such, practitioners may benefit from a framework that can help guide metric selection for commonly used jump tests, which is the primary purpose of this article. To contextualize the proposed framework, we have provided 2 examples for how this could work: one for the CMJ and one for the DJ, noting that these tests are commonly used by practitioners during routine testing across a range of sport performance and clinical settings.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 it