Atmospheric turbulence, meteorological modeling and aerodynamics
Bibliographic record
Abstract
Preface Atmospheric Turbulence: Prediction, Measurement, and Effects. Climatology of the Arctic Planetary Boundary Layer Nonequilibrium Thermodynamic Theory Of The Atmospheric Turbulence Generalized Scale Invariance of Edge Plasma Turbulence Turbulence, Turbulent Mixing and Diffusion in Shallow-Water Estuaries Turbulence Scalar Transfer Modeling in Reacting Flows Large-Eddy Simulation of Free Shear and Wall-Bounded Turbulent Flows The research turbulence method with using sensitivity?of the solution quality for the k- analysis of flow properties to model coefficients Passive Air Sampler for the Determination of Atmospheric Nitrogen Dioxide Using Flat Porous Polyethylene Membrane as Turbulence Limiting Diffuser Artificial Intelligence Technique for Modelling and Forecasting of Meteorological Data: A Survey A Semi-Analytic Model of Fog Effects on Vision New Trends on Phenological Modelling Time Dependent Shape Optimization Using Adjoint Variable Method for Reducing Drag Numerical and Experimental Investigations of Fluid Dynamics of High speed Flows Aerodynamic Research and Development of Vertical-Axis Wind Turbines with Rotary Blades Grand Computational Challenges for Prediction of the Turbulent Wind Flow and Contaminant Transport and Dispersion in the Complex Urban Environment The Determination of Aerodynamic Forces on Sails -- Challenges and Status On parameterising inclined stable boundary layers Ambient Air Temperature Interpolation in Inhomogeneous Regions Peat Moisture in Relation to Meteorological Factors: Monitoring, Modelling, and Implications for the Application of the Canadian Forest Fire Weather Index System Index.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.018 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".