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Record W1977922280 · doi:10.1175/jamc-d-11-0190.1

Measurements of Enhanced Turbulent Mixing near Highways

2012· article· en· W1977922280 on OpenAlexaffabout
Mark Gordon, Ralf M. Staebler, John Liggio, Paul A. Makar, Shao‐Meng Li, Jeremy J. B. Wentzell, G. Lu, Patrick Lee, Jeffrey R. Brook

Bibliographic record

VenueJournal of Applied Meteorology and Climatology · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsTurbulence kinetic energyTurbulenceEnvironmental scienceMeteorologyKinetic energyAtmospheric sciencesIntensity (physics)Flow (mathematics)MechanicsPhysics

Abstract

fetched live from OpenAlex

Abstract In August and September of 2010, measurements of turbulent fluxes and turbulent kinetic energy were made on highways in the Toronto area (Ontario, Canada). In situ turbulence measurements were made with a mobile laboratory while driving on the highway with traffic. Results demonstrate that the turbulent kinetic energy (TKE) spectrum is significantly enhanced on and near the highway by traffic for frequencies above 0.015 Hz. The decay of TKE with distance behind vehicles is well approximated by power-law curves. The strongest increase in TKE is seen while following heavy-duty trucks, primarily for frequencies above 0.7 Hz. From these results, a parameterization of on-road TKE enhancement is developed that is based on vehicle type and traffic-flow rate. TKE with distance downwind of the highway also decays following a power law. The enhancement of roadside TKE is shown to be strongly dependent on traffic flow. The effect of vehicle-induced turbulence on vertical mixing was studied by comparing parameterized TKE enhancement with the typical TKE predictions from the Global Environmental Multiscale weather forecast to predict the potential increase in vertical diffusion that results from highway traffic. It is demonstrated that this increase in TKE by traffic may be locally significant, especially in the early morning.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.090
Threshold uncertainty score0.343

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.019
GPT teacher head0.238
Teacher spread0.219 · 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 designBench or experimental
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

Citations33
Published2012
Admission routes2
Has abstractyes

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