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Record W2512666317 · doi:10.1136/oemed-2016-103951.615

P300 Using line of sight plots to increase awareness of visibility hazards

2016· article· en· W2512666317 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSimulation and Modeling Applications
Canadian institutionsLaurentian University
Fundersnot available
KeywordsVisibilityComputer scienceSightLine-of-sightLine (geometry)Computer visionGeographyEngineeringAstronomyMathematicsMeteorologyPhysics

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.384
Threshold uncertainty score0.167

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.039
GPT teacher head0.310
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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