Bibliographic record
Abstract
Turbulent diffusion is an important research direction in the turbulent phenomenon.Phenomenological description of turbulent diffusion is a complex and still unsolved problem [1]- [2].This article devoted to mathematical model for the turbulent convective Diffusion in the Free Atmosphere.The main goal of this research is to build a mathematical apparatus for studying atmospheric turbulence, determining its main characteristics, influence of divergence and rotor operators on the particle velocity vector, processing pressure distribution, diffusion temperature and ideal gas particle concentration.Here real gases differ from their ideal model if their molecules have finite dimensions and between them are forced attraction at considerable distances and repulsion when the molecules approach each other.An ideal gas does not interact between particles and consists of point particles phenomena moving randomly or following Brownian motion.There are could be air, gas methane, helium, nitrogen, argon, propane, oxygen.Any real gas is closer to ideal when lower its pressure then higher its temperature.The introduced mathematical model has been described by using momentum conservation equation from which the Navier-Stokes initial value problem was solved by using vector analyse and mathematical physic methods.The three-dimensional particle velocity and the energy conservation law was determined and from which the scalar pressure distribution was found [3]- [4].Wherein the determined expression
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".