Supplementary material to "A Modified Gaussian Plume Model for Mobile in situ Greenhouse Gas Measurements"
Bibliographic record
Abstract
S1. Developing the asymmetric correction functionFrom the controlled release experiment, we noticed the severity of the asymmetric smoothing to the observed plumes caused by the LGR UGGA's lower flow rate and high mean residence time.The function we used to model the instrument response is an asymmetric function which is similar to a lognormal distribution.This function was chosen because the characteristic variables, σ and μ, move the function's vertical and horizontal components of the local maxima independently.We then determined a linear speed dependence for the hand fitted μ parameters which best matched observed concentrationdistance plumes from the controlled release experiment with a simple linear regressions.Then, we determined a linear relation between the fitted σ and μ values.These speed dependent smoothing factors were then extrapolated to zero velocity, and then these parameter values ( σ = 0.517, μ = 2.57) were taken to the time ordinate parameters, or smooth ing coefficients concentration-time inversion tests.Middling values between the fitted parameters, ( σ = 0.65, μ = 2.2) were used for the concentration-distance plumes.Both are shown in Figure S1.We normalize the function across a specified window length, which when convolved with an enhancement plume shape, results in an asymmetrically skewed curve with the same enhancement area as the original curve. S1.1 Asymmetric smoothing and comparing quasi-coincidental plume transects.During another mobile field campaign measuring plumes at the Petrolia landfill near Petrolia, Ontario, the LGR UGGA instrument was deployed in a rented vehicle as a mobile GHG labratory.The same Airmar WX220 was used as a GPS receiver in this setup.To compare quasi-coincidental observations, the UGGA equipped vehicle drove ~30m behind the ECCC Picarro vehicle through the same methane plume from transects recorded at 17:11 UTC on 2021-09-19 1 .We determined a temporal offset to align centre of each peak, and then convolved a the Picarro plume, interpolated to 1 second intervals, with the smoothing windows, and the results are shown in Figure S1.The r 2 coefficient for the observations increases from 0.29 for the Picarro observations and the UGGA , to 0.9 for the smoothed plume.This represents a significant improvement in the comparability of quasi-coincidental observations.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.302 | 0.056 |
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".