Dog agility tunnel risks for incidents
Bibliographic record
Abstract
Background: Flexible tunnels are the second most common obstacle on all dog agility courses, surpassed only by jumps. There has been a lot of debate and concern regarding risk factors associated with slips, falls and delayed exits (unseen slips, missteps, trips, falls). However, only one study was found which focused on the tunnel-related injuries, and it relied on handler reporting and did not consider base rates of the risk factors. As such, it is currently unknown which risk factors are statistically predictive of incidents. This study addresses this gap. Methods: Observational data from local, regional, national and international agility competitions (between June 30, 2023, to September 22, 2024) were collected from various agility organizations and countries by a team of researchers who are also judges and/or coaches within the sport. Tunnel, equipment, competition and course attributes, ground type and conditions along with tunnel incidents (slips, falls, and delayed exits) were recorded. Correlation, regression analyses, and chi-squared tests of independence were conducted to identify the relevant factors associated with incident rates. Results: The data included 563 tunnels (75.0% were incident free), with 30,418 tunnel performance observations (1.552% were incidents). The identified factors associated with incidents include tunnel characteristics (equipment specifications, shape on course), type and density of fixtures, course design (shape in design, angle of approach), ground and conditions. Their association with incident occurrence will be further detailed below. Discussion: Several previously assumed risk factors were relevant; however, some were not supported, and additional new factors were identified. Implications for future research and for organizations, judges, trial hosts, and competitors are discussed.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".