Observations and Modeling of Heavy Particle Deposition in a Windbreak Flow
Bibliographic record
Abstract
Abstract This paper presents new observations of deposition of heavy particles (glass beads of gravitational settling velocity 8.7 cm s−1) within an undisturbed flow and within a flow disturbed by a porous windbreak fence. These data are then used to diagnose the capability of a Lagrangian stochastic (LS) particle trajectory model, which simulates heavy particle dispersion. The model is based on existing parameterizations and is coupled to a wind model based on a Reynolds stress turbulence closure that provides computed fields of wind statistics. The deposition rates, as simulated by the model, match the observation within E = 30% of accuracy, with E being the root-mean-square error normalized by the peak value on the deposition swath. These results suggest that the LS model handles properly the heterogeneities of the flow and that the heuristic adjustments made to account for the inertia of heavy particles are useful approximations. The model consequently proves to be a valuable tool to investigate the patterns of dispersion about an obstacle.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".