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Record W2050424232 · doi:10.1680/muen.2008.161.2.129

The risk of tripping accidents on public footways

2008· article· en· W2050424232 on OpenAlexfundno aff
Stephen R. Bird

Bibliographic record

VenueProceedings of the Institution of Civil Engineers - Municipal Engineer · 2008
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Maintenance and Monitoring
Canadian institutionsnot available
FundersCMG Reservoir Simulation Foundation
KeywordsJudgementHazardTrippingForensic engineeringRisk analysis (engineering)Accident (philosophy)Transport engineeringRisk assessmentActuarial scienceComputer scienceOperations managementBusinessEngineeringComputer securityPolitical science

Abstract

fetched live from OpenAlex

Highway authorities maintain footways in a safe condition following a regime of regular inspections, repairing hazardous defects found and resurfacing at less frequent intervals. The interval between inspections depends on the footway usage, and the reaction time for repairs depends on the hazard posed by the defect as well as the footway usage, reflecting the relative risk of an accident. Currently, these intervals are determined by judgement and this paper describes how the risk can be quantified. Records of third-party claims were examined for factors that influence numbers of accidents, including pedestrian age, defect size and footway construction. Statistics of accidents requiring hospital treatment and the results of medical research into walking provided further insight. By making a number of assumptions, a relationship between risk and defect height was derived. The cost to society of a footway accident was also determined. Thus, for a given footway network and maintenance regime, the likely number of accidents and their cost can be calculated. This enables highway authorities to compare the costs of different maintenance regimes with the benefits of accidents prevented. Collecting further data in a standard format would enable refinement of the model.

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.

How this classification was reachedexpand

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.644
Threshold uncertainty score0.730

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.012
GPT teacher head0.197
Teacher spread0.185 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Institution of Civil Engineers - Municipal EngineerSame topicInfrastructure Maintenance and MonitoringFrench-language works237,207