The effects of sport specific training of rugby players on avoidance behaviours during a head-on collision course with an approaching person
Bibliographic record
Abstract
The current study aimed to examine if sport-specific training has an impact on the avoidance behaviours of rugby players during a head-on collision course with an approaching person. Female rugby players (N=10, x ?= 20 ± 0.94 years) and non-athletes (N=10, x ?= 21.9 ± 1.6 years) were instructed to walk along a 10m path towards a goal located along the midline. A female confederate initially positioned along the midline 180? from the participant walked towards the participant to one of four predetermined final positions: 1) along the midline in the participants' starting position; 2) stopped along the midline 2.5 m from her starting position; 3) to the left of the participants' starting position; and 4) to the right of the participants' starting position. Results revealed when the path of the confederate was uncertain, individuals used a consistent TTC to determine when to change their path. Athletes were found to avoid significantly later (i.e. smaller TTC) than non-athletes. However, following a change in path, sport-specific training did not impact the avoidance behaviours of the groups, but rather the environment was the regulating factor. When the path of the confederate was uncertain, individuals did not use a single avoidance strategy, instead considered the fit between their individual characteristics (i.e., action capabilities) and components of the environment (i.e. path of the confederate and task constraints). Athletes who are specifically trained to fit between spaces and avoid obstacles may consider their action capabilities in conjunction with their visual information to determine time of avoidance.Acknowledgments: NSERC 05288-2014
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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 | 0.001 |
| 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) | 0.002 | 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".