Bibliographic record
Abstract
How parkour enthusiasts scramble up wallsWhen Sebastian Foucan burst onto our screens in the opening sequence of Casino Royale as a terrorist pursued by James Bond through a building site, the gravitydefying art of freerunningand parkour, its more formal predecessorhit the mainstream.Scrambling up vertical steel girders, bounding effortlessly across roof tops and leaping down stairwells, Foucan's acrobatics are breathtaking.However, when James Croft from Edith Cowan University, Australia, watches parkour enthusiastsknown as traceurshe wants to understand how they use their bodies to pull off such daredevil stunts.'Currently, we depend on interpretations of leg action based on how we move when running normally', says Croft, but running up a vertical wall presents a completely unique set of challenges.Having worked previously with John Bertram from the University of Calgary, Canada, to understand why traceurs crumple into a roll after dropping from height, the duo reunited to find out how the daring athletes scale a wall.10.1242/jeb.196592
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.015 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.028 | 0.014 |
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".