Wearable technology for design and safety evaluation of rider acceleration exposure on Zip Line attractions
Bibliographic record
Abstract
Aerial adventure attractions are intended to produce exhilarating sensations at significant elevation, speed, and acceleration, all while maintaining the safety of the participant. While Zip Line designers and owners can refer to international standards addressing many safety requirements, the measurement and assessment of acceleration exposures of the Zip Line rider has not been standardized. My major research project (MRP) considers the design and validation protocol for wearable sensor technology to collect acceleration and g-force exposure of a Zip Line rider. Introducing the combination of systematic design and quantitative analysis to wearable technology architectures requires considerable thought taking into account existing ride standards, biomechanics, ergonomics and the need for data accuracy. The primary objectives are two-fold, 1) contribute a test protocol that will evaluate the reliability and validity of the proposed system, and 2) take a step forward towards implementing a consistent process to capture acceleration exposure on Zip Line attractions. We contribute a test protocol that will evaluate the reliability and validity of the proposed system.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".