57 Methodology validation of using motion-activated cameras to estimate off-highway vehicle park census data and evaluate rider safety behaviours
Bibliographic record
Abstract
Statement of purpose Safety behaviours of off-highway vehicle (OHV) park users may be better than those riding elsewhere, and census data is required to determine if differences among OHV parks in injuries can be explained by higher use or if other factors are involved. This study’s purpose was to ascertain the photographic capture rate of riders entering OHV parks with motion-activated cameras, and to determine study variable concordance between on-site evaluations and photo appraisement. Methods/approach Panoramic motion-activated cameras were placed at the entrances of all eight public OHV parks in Iowa. On-site assessment and photo image analysis was performed for demographic, vehicle, and safety behaviour variables, and then compared. Results On-site data collection was performed for 114 h for 17 cameras during which 493 off-road vehicles entered the OHV parks. A total of 251 vehicles entering the parks were identified on photos for an overall capture rate of 50.9%. For cameras with at least 5 vehicles noted during on-site evaluation, the capture rate ranged from 38–81%. The number of vehicles entering the parks was segmented by 15 min intervals, and there was no significant variance or decrease in photo capture rate until there were more than 15 vehicles entering a park during a quarter hour period. Variable concordance between on-site and photo derived data ranged from 91–100% which included sex of rider, estimated age group of rider, vehicle type, number of wheels, number of vehicle riders, helmet use, and restraint use for side-by-side vehicles. Conclusions About one-half of riders entering Iowa OHV parks were captured by motion-activated cameras. Concordance of study variables was high between that collected on-site and from photo assessment. Significance and contributions The study validated the use of photo-derived data to determine OHV park users and safety behaviours. Results will also assist in park census estimation.
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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 | 0.032 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".