Bibliographic record
Abstract
The current thesis investigates the contribution of aerodynamic drag on an elite sprint kayaker as a factor in the outcome of a race, where a thousandth of a second can be the increment of success.For kayaking, field of play conditions are an important consideration, as the overall aerodynamic drag on a kayaker comes from the kayaker's forward motion and local atmospheric boundary layer (ABL) winds.The drag on a kayaker due to ABL winds depends on the wind speed, which is expected to follow a non-uniform profile.To establish the reliability of a theoretical profile for wind over water at a sprint kayaking venue, field testing has been carried out showing that the wind speed profile within the height of a kayaker followed the theoretical logarithmic curve for over 80% of tests in light winds.The aerodynamic drag of a model kayaker was then measured in a wind tunnel, where a range of inflow profiles were used to confirm that the kayaker's aerodynamic drag coe cient, C D , is primarily sensitive to velocity profile, with a small sensitivity to Re for the subcritical range from 1.0 ⇥ 10 5 to 2.7 ⇥ 10 5 .The confirmation of the dominance of the velocity profile in comparison to other airflow e↵ects including turbulence, multi-component flow interaction and shape e↵ect, was shown by normalizing the C D for each inflow condition.For an inflow profile representing the prevailing ABL logarithmic profile confirmed from field testing, the normalized C D was found to collapse towards the uniform-flow C D to within 1%.The wind-tunnel findings with respect to the trends in the aerodynamic drag coe cient were then applied to a single case for a men's K1 200 m race.Results showed that for a calm atmosphere a reduction in air drag coe cient of 10% can correspond to a decrease in finishtime of 0.1 s.When ABL wind drag is added, results imply that for a headwind ranging from iii I would like to acknowledge the financial support from the Natural Science and Engineering Research Council of Canada (NSERC) Discovery grant program which was awarded to Dr. Guy Larose, without which, this research would not have been possible.I am grateful to Own The Podium, specifically Jon Kolb, Kelly McKean and Leo Thornley for supporting the advancement of sprint kayaking science by sharing their wind tunnel and tow tank test results with me and answering all questions on the sport, no matter how small.It is this group that has given purpose to the present research by providing relevant questions and concerns.Thank you to the Blu↵ Body Group and Caroline Muselet at the National Research Council of Canada (NRC) who have freely answered questions which improved my research work significantly.A special thanks to Sean McTavish, who has been there as a mentor and friend throughout, and who
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".