MétaCan
Menu
Back to cohort
Record W2022225475 · doi:10.1103/physreve.70.036307

23/9 dimensional anisotropic scaling of passive admixtures using lidar data of aerosols

2004· article· en· W2022225475 on OpenAlexaff
M. Lilley, S. Lovejoy, K. B. Strawbridge, Daniel Schertzer

Bibliographic record

VenuePhysical Review E · 2004
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsMcGill University
Fundersnot available
KeywordsIsotropyScalingAnisotropyMesoscale meteorologyPhysicsStratification (seeds)Scalar (mathematics)LidarBuoyancyMeteorologyTurbulenceStatistical physicsMechanicsGeometryOpticsMathematics

Abstract

fetched live from OpenAlex

In buoyancy-driven flows, another dimensional quantity appears in addition to the energy flux. Classically, this leads to the prediction that at large scales, isotropic Bolgiano-Obukhov (BO) scaling can dominate isotropic Kolmogorov scaling. We investigate this in the atmosphere by using state-of-the-art high-powered lidar data. We examine simultaneous horizontal and vertical sections of passive scalar surrogates over the ranges 100 m to 120 km and 3 m to 4.5 km , respectively. Overall, this spans the crucial "mesoscale" and involves nearly 1000 times more data than the largest relevant experiments to date. Rather than a transition from one isotropic regime to another, we find that the two regimes always coexist in an anisotropic Corrsin-Obukhov law with the Kolmogorov holding in the horizontal, and the BO holding in the vertical. The stratification is quantified by an elliptical dimension D(el) found to be equal to 2.55+/-0.02 . This anisotropic scaling is very close to that predicted by the 23/9 dimensional unified scaling model of the atmosphere and is consistent with observations of the horizontal wind.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.434
Threshold uncertainty score0.421

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.027
GPT teacher head0.290
Teacher spread0.263 · 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

Citations51
Published2004
Admission routes1
Has abstractyes

Explore more

Same venuePhysical Review ESame topicFluid Dynamics and Turbulent FlowsFrench-language works237,207