Deducing Ground-to-Air Emissions from Observed Trace Gas Concentrations: A Field Trial with Wind Disturbance
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Bibliographic record
Abstract
Abstract Inverse-dispersion techniques allow inference of a gas emission rate Q from measured air concentration. In “ideal surface layer problems,” where Monin–Obukhov similarity theory (MOST) describes the winds transporting the gas, the application of the technique can be straightforward. This study examines the accuracy of an ideal MOST-based inference, but in a nonideal setting. From a 6 m × 6 m synthetic area source surrounded by a 20 m × 20 m square border of a windbreak fence (1.25 m tall), Q is estimated. Open-path lasers gave line-averaged concentration CL at positions downwind of the source, and an idealized backward Lagrangian stochastic (bLS) dispersion model was used to infer QbLS. Despite the disturbance of the mean wind and turbulence caused by the fence, the QbLS estimates were accurate when ambient winds (measured upwind of the plot) were assumed in the bLS model. In the worst cases, with CL measured adjacent to a plot fence, QbLS overestimated Q by an average of 50%. However, if these near-fence locations are eliminated, QbLS averaged within 2% of the true Q over 61 fifteen-minute observations (with a standard deviation σQ/Q = 0.20). Poorer accuracy occurred when in-plot wind measurements were used in the bLS model. The results show that when an inverse-dispersion technique is applied to disturbed flows without accounting for the disturbance, the outcome may still be of acceptable accuracy if judgment is applied in the placement of the concentration detector.
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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) | 0.001 | 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 it