P300 Using line of sight plots to increase awareness of visibility hazards
Bibliographic record
Abstract
Operators of large mobile equipment must contend with limited line of sight (LOS), and must use varying levels of awkward postures to obtain enough LOS to safely manoeuvre in the workplace. Anecdotally, individuals who have never driven large mobile equipment (ie. first responders, supervisors, investigators and other pedestrians) are unaware of the extent of the limited LOS, despite their frequent interactions with such equipment. The safety of pedestrians on worksites hinges on their understanding of the visibility limits of the operators driving around them. Most drivers of passenger cars have an appreciation for their lack of visibility when passing through the blind spots of large transport trucks. This same awareness does not seem to extend to the workplace. A pen and paper test was developed in an effort to assess the mismatch between a pedestrian’s perception of equipment LOS and the actual LOS available to the operator. Participants with no previous experience operating large mobile equipment were shown a scaled representation of a haul truck, Euclid EH4500, and were then asked to complete a LOS plot diagram representing the LOS an operator may have, purely based on their interpretation of the external appearance of the equipment. The actual line of sight plot for that machine was then overlaid, and percentage of area overlap was compared to the participants’ diagrams. The participants were then given the standard LOS plot, and were given 10 minutes to examine it. Seven days later the participants were asked once again, without any visual aids or reminders of the machine, to draw a LOS plot for the same haul truck. This investigation evaluates the usefulness of LOS plots as a potential low-cost high-impact intervention to increase awareness and knowledge of the health and safety implications of visibility.
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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 | 0.008 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".