Simulation of odour dispersion downwind from natural windbreaks using the computational fluid dynamics standard <i>k</i>-<i>ε</i> modelA paper submitted to the Journal of Environmental Engineering and Science.
Bibliographic record
Abstract
If natural windbreaks create air turbulence and can help disperse odours from livestock operations, their dispersion effect has not been extensively researched. This paper introduces a model simulating odour dispersion downwind from natural windbreaks. The standard k-ε model of the FLUENT software was used to incorporate the effect of climatic conditions and windbreak structure. The model was calibrated for wind velocity recovery, odour concentration (OC) from field observations and inertial resistance. Once calibrated, the model accurately reproduced the odour plume developing downwind from the windbreak. The correlation between OC for 11 field trials and that simulated were statistically significant (P < 0.01), indicating that the model was accurate. The model demonstrated that a windbreak did alter the magnitude and direction of the wind velocity, thus creating a pressure jump across its width. This pressure jump produces a strong downwind turbulence forming a mixing layer capable of enhancing odour dispersion.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".