A BIOMECHANICAL AND PHYSIOLOGICAL COMPARISON OF OLYMPIC FLATWATER CANOEING
Bibliographic record
Abstract
Simulation of sporting activities for the purpose of assessing physiological parameters and for conditioning athletes has been an important development within the sports world. The purpose of this study was to compare Olympic flatwater canoeing technique to that of an ergometer developed by Pyke et al. at Dalhousie University. The comparison, using three national team members, was both physiological and biomechanical in order to determine; 1) if accurate physiological measurements focusing on the upper body during racing conditions could be matched while using the laboratory ergometer; 2) if the ergometer movement patterns closely approximated the actual on-water racing stroke. The results indicated that the techniques were similar physiologically and different biomechanically. VE and VO2 max, for the 500 m. race and for a simulated 500 m. trial were close and consistent across all S's. Results for the 1000 m. were acceptable, but not as accurate as the 500 m. The use of the Pyke ergometer was judged on the whole to be a valid physiological testing procedure. The major difficulty with the ergometer was that it forced all S's to alter their racing strokes in order to successfully maintain movement of the mechanism. Changes in movement and velocity patterns of the trunk, arms and hands of all S's were considerable and led to the conclusion that this ergometer, in its original design, not be used as a training device.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".