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Record W4247188483 · doi:10.32920/ryerson.14645970

The effect of traffic strategies on emissions

2021· preprint· en· W4247188483 on OpenAlexaff
Mohammad Orfi

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicVehicle emissions and performance
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsIntersection (aeronautics)Environmental scienceAir quality indexAir pollutionPlan (archaeology)NOxAgency (philosophy)Nitrogen oxidesTransport engineeringEnvironmental engineeringEnvironmental economicsMeteorologyEngineeringGeographyWaste managementEconomicsChemistry

Abstract

fetched live from OpenAlex

Air pollution and its relationship to the ecosystem and human life has always been the subject of a significant amount of study. The effect of highway air emissions on urban air quality has been studied for many years. This report contains a simulation of a single intersection in an urban area, using Arena®, a general purpose simulation program, and taking into account dynamic and stochastic considerations. The United States Environmental Protection Agency (USEPA) emission factors for idling situations were used to measure the emission of carbon monoxide (CO), volatile organic compounds (VOC) and nitrogen oxide (Nox) for the delay time. The simulation result predicts emission levels to be higher in a two-phase plan (unprotected left lane) with three different cycle times studied in this case (90, 120, and 140 seconds) compared to a three-phase plan (a protected left lane). However, the degree to which a two-phase plan is positively correlated with intersection cycle time suggests that a multi-faceted approach needs be taken in implementing modifications to reduce overall emissions.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.008
GPT teacher head0.242
Teacher spread0.234 · 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 designObservational
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
Published2021
Admission routes1
Has abstractyes

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