The effects of a human obstacle and form of locomotion on the path selection of women's rugby players
Bibliographic record
Abstract
INTRODUCTION: Obstacle avoidance behaviours are affected by either an individual's previous training or the environment. Athletes trained in fitting between spaces have more refined avoidance behaviours during training-specific situations. Recent research demonstrates that avoidance behaviours are more cautious with animate obstacles than inanimate. The current study investigated the effects of athletic training and location of an animate obstacle in conjunction with path selection. METHODS: Female rugby players were instrumented with IRED markers on the head and trunk to calculate Centre of Mass (COM) over time. Participants were instructed to: 1) walk; 2) walk with the ball; or 3) run with the ball along a 10m path toward a goal located along the midline. Three obstacles were placed at 5m, perpendicular to the pathway, consisting of either three vertical poles or two vertical poles and a confederate. The obstacles were separated by 80cm, creating two equal apertures on either side of the midline. The location of the confederate was either: 1) along midline; 2) 80cm to the left; 3) 80cm to the right; or 4) not present. RESULTS: The effects of a human obstacle and form of locomotion on path selection were examined by analyzing participants' COM. The results indicated that rugby players will choose paths furthest from the confederate (F(3,30)=31.p
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 | 0.000 | 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".