Contribution of trunk swing to the performance of fixed-seat rowing
Bibliographic record
Abstract
Introduction This study aimed to test the contribution of trunk swing to the performance during fixed-seat rowing in eligible and non-eligible (NE) para rowers. Assessment of trunk swing is used to classify para rowers with physical disability in Para Rowing (PR) 1 and PR2 rowers. PR1 rowers are classified based on the demonstration of impaired function of trunk swing. Methods PR1, PR2, and NE rowers participated. Rowing ergometers were used in two different fixed-seating conditions, resulting in either (1) restricted trunk swing or (2) unrestricted trunk swing during the rowing stroke. Participants performed maximal effort 500 m pieces (race pace) in each seating configuration. Force production at the handle and fixed-seat rowing-specific trunk extension force was measured. Rowing performance measures were compared using a repeated-measures general linear model, including condition and group and an interaction between condition/group. Results Only PR1 rowers generated greater trunk extension force during the restricted condition compared with the unrestricted trunk condition (P < 0.01). The restricted trunk swing condition resulted in a faster time to complete 500 m and minimal impact on force production for PR1 rowers. NE and PR2 rowers showed a significantly faster time to complete 500 m and greater stroke impulse (Ns) in the unrestricted compared with the restricted trunk swing condition (P < 0.01). Discussion These results provide evidence-based reasoning for the classification of fixed-seat rowers. Contrary to PR2 and NE rowers, whose rowing performance was decreased due to trunk restriction, PR1 rowers' performance benefits from the trunk restriction.
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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 | 0.002 |
| 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) | 0.003 | 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".