Evaluation of Fiberglass and Aluminum Ladder Stability During a Simulated Tethered Operator Fall Event
Bibliographic record
Abstract
Ladder-related falls are common in many industries and lead to high rates of injury and hospitalization. This study aimed to establish safety criteria for ladder stability and potential for failure in the event of an operator fall when equipped with a fall arrest system that has been attached to the ladder as opposed to a fixed structure anchor point. All combinations of five variables were tested in a custom-built load drop apparatus: ground surface, leaning surface, force direction, operator tether method, and ladder type. Overall, all conditions tested in this investigation with a simulated worker falling from a ladder with the fall arrest device attached to the ladder rails or a rung resulted in a pass. While there were clear deformations to the aluminum ladders and there were 3 failures due to repeated drop tests on the same ladder, replication of failed tests on a new ladder passed the failure conditions. Given a ladder that is in good working condition, has not be subjected to prior falls or damage, is properly erected and secured - a worker with a mass below 113kg (250lb) would be safely restrained when tethered to the ladder as opposed to the lanyard being tied off to a structure or a lifeline based on the simulated mass drops performed in this study in controlled laboratory conditions.
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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) | 0.001 | 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".