Ground-to-Air Gas Emission Rate Inferred from Measured Concentration Rise within a Disturbed Atmospheric Surface Layer
Bibliographic record
Abstract
Abstract In reference to previously observed concentrations of methane released from a source enclosed by a windbreak, this paper examines a refined “inverse dispersion” approach for estimating the rate of emission Q from a small ground-level source, when the surface-layer winds near that source are highly disturbed. The inverse dispersion method under investigation is based on simulation of turbulent trajectories between sources and detectors, using a Lagrangian stochastic (LS) model. At issue is whether it is advantageous to recognize the flow as being disturbed and use a computed approximation to that disturbed flow to drive a fully three-dimensional LS model (3D-LS), or whether it suffices to ignore flow disturbance and adopt an LS model attuned to the horizontally homogeneous upwind flow (MO-LS, as Monin–Obukhov similarity theory describes the vertical inhomogeneity). It is demonstrated that both approaches estimate the source strength to within a factor of 2 of the true value, irrespectively of the location of the concentration measurement, and moreover that both approaches estimate the source strength correctly (to within the experimental uncertainty), when based on concentrations measured far away from the immediate influence of obstacles in the flow. However, if the concentration detector is positioned close to the flow-disturbing obstacles, then inverse dispersion based on 3D-LS provides a better estimate of source strength than does MO-LS.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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 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".