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Record W657754062

Modification of stopping sight distance and length of crest vertical curve using air resistance and drag force parameters

2011· article· en· W657754062 on OpenAlexaboutno aff
Swapan Kumar Bagui, Ambarish Ghosh

Bibliographic record

VenueIndian highways · 2011
Typearticle
Languageen
FieldEngineering
TopicTraffic Prediction and Management Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsDragBraking distanceAerodynamic dragSightDrag coefficientWind speedAerodynamicsSimulationAccelerationCrestMeteorologyEnvironmental scienceMarine engineeringAerospace engineeringGeodesyComputer scienceEngineeringPhysicsAutomotive engineeringGeologyOpticsClassical mechanics
DOInot available

Abstract

fetched live from OpenAlex

There are two parts to Stopping Sight Distance (SSD): Distance traveled during reaction time and Braking distance. It is the standard practice, according to Indian Roads Congress (IRC), American Association of State and Highway Transportation Officials (AASHTO), Australian, and Canadian guidelines, to determine SSD based on these two distances. However, these practices do not take into account the role of air resistance, drag force and wind speed on SSD. A vehicle will move from one place to another after removing air. Air resistance reduces the sight distance, as does drag force. Wind velocity reduces SSD in cases in which the wind moves in the opposite direction of vehicle movement and vice versa. SSD is used to determine the length of the vertical curve. Using all these parameters, including aerodynamic drag coefficient, frontal area of vehicle and mass of vehicle, SSD has been modified. Reported herein are SSDs for various situations.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.030
GPT teacher head0.217
Teacher spread0.187 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2011
Admission routes1
Has abstractyes

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