Quantifying Equestrian Show Jumping: A Large Context Problem for Physics Students
Bibliographic record
Abstract
ASEJ, Volume 43, Number 1, June 2013 Equestrian show jumping has become a popular spectator sport in Canada since the Beijing Olympic Games in 2008. Canada received a gold medal in singles and a silver in team competition. Eric Lamaze and his stallion Hickstead, generally regarded as the best showjumping horse of his generation, became internationally famous, and Lamaze was ranked first in the world. Unfortunately, three years later Hickstead suddenly died in Verona, Italy, after jumping a clear round. This tragic event plunged the equestrian community into deep mourning. These events reawakened the love for horses I acquired when working in the forestry industry in British Columbia as a young man. As a physics educator, I naturally became interested in the physics of the jumping motion of these magnificent animals. I remember a letter written by an irate reader of the British journal New Scientist in response to my article “Physics and the Bionic Man” (Stinner 1980). The gentleman argued that my testing the feats claimed by the bionic man, using the laws of physics, spoiled the enjoyment of many devotees of the popular TV series The Six Million Dollar Man. As students of physics, we can always appreciate the aesthetics of phenomena such as rainbows and sunsets, but understanding the physics should enrich our aesthetic appreciation. Similarly, equestrian show jumping can be appreciated on more than one level.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".