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Record W4256311977 · doi:10.32920/ryerson.14663364.v1

Analysis of passing sight distance using first-order reliability method

2021· preprint· en· W4256311977 on OpenAlexafffund
Andres Serrano

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicInfrastructure Maintenance and Monitoring
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSightMoment (physics)Reliability (semiconductor)Computer scienceVariable (mathematics)Measure (data warehouse)SimulationMathematicsStatisticsAlgorithmMathematical analysisData mining

Abstract

fetched live from OpenAlex

Current passing sight distance requirements for two lane highways by the American Association of State Highway and Transportation Officials are based on field studies conducted between 1938 and 1941 which use deterministic values for its design variables such as passing sight distance, speed of the passing vehicle, speed differential between the passed and passing vehicle etc. This report presents three methods to analyze reliability and serves as an extension to the revised model presented by Yasser Hassan, Said Easa and A.O.Abd El Halim whose model sought to improve older models by equally considering both observed passing behaviours of drivers and passing maneuvers that are consistent with two lane highways. Analysis of passing sight distance using first order second moment reliability method, advanced first order second moment and the ellipsoid approach to measure the probability of failure of the passing sight distance design, rely solely on the mean and variance (moments) of each randomly distributed variable in contrast to methods that rely only on deterministic values. Results show the advanced first order second moment and the ellipsoid approach provided more accurate results than first order second moment method which in turn provide a greater safety margin with the later also proving to be a much more robust and efficient method of performing a reliability

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.567
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.272
Teacher spread0.260 · 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.

Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations1
Published2021
Admission routes2
Has abstractyes

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