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Record W3134012516

Estimation of mixing in the troposphere from Lagrangian trace gas reconstructions during long-range pollution plume transport

2009· article· en· W3134012516 on OpenAlexaff
Ignacio Pisso, E. Real, Kathy S. Law, Bernard Legras, Nicolas Bousserez, Jean‐Luc Attié, Hans Schlager

Bibliographic record

VenueEGU General Assembly Conference Abstracts · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsDalhousie University
Fundersnot available
KeywordsTropospherePlumeThermal diffusivityMixing (physics)Trace gasTRACERAtmospheric sciencesEnvironmental scienceRange (aeronautics)TurbulenceDispersion (optics)Eddy diffusionMeteorologyGeologyPhysicsMaterials scienceThermodynamics
DOInot available

Abstract

fetched live from OpenAlex

The dispersion and mixing of pollutant plumes during long-range transport across the North Atlantic is studied using ensembles of diffusive backward trajectories in order to estimate turbulent diffusivity coefficients in the free troposphere under stratified flow conditions. Values of the order of 0.3-1 m2 s−1 and 1 × 104 m2 s−1 for the vertical and horizontal diffusivity coefficients D v and D h , respectively, are derived. Uncertainties related to the method are discussed, and results are compared with previous estimates of atmospheric mixing rates. These diffusivity estimates also yield an estimate of the vertical/horizontal aspect ratio of tracer structures in the troposphere. Results from this case study are used to estimate grid resolutions needed to accurately simulate the intercontinental transport of pollutants as being of the order of 500 m in the vertical and at least 40 km in the horizontal. This work forms the basis of high-resolution chemical simulations using ensembles of diffusive backward trajectories.

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)0.001
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 score0.024
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.013
GPT teacher head0.216
Teacher spread0.203 · 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

Citations1
Published2009
Admission routes1
Has abstractyes

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