Estimating Tracer Emissions with a Backward Lagrangian Stochastic Technique
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This chapter looks at one type of application of the inverse-dispersion method: the estimation of tracer emissions from a discrete surface area source, using concentration observations taken near the source (within 1 km). This might include emissions from small soil treatment plots, feedlots, ponds, landfills, industrial grounds, etc. The chapter focuses on situations where the terrain is “tolerably” homogeneous, and amenable to a Monin-Obukhov similarity description of the surface winds. The advantages of the inverse-dispersion method for these problems are experimental simplicity, the absence of limitations on the size and shape of the source, and flexibility in the type and location of the concentration measurement used to infer emissions. The accuracy of the method rests on having an accurate atmospheric dispersion model. The chapter describes a promising avenue for application of the inverse-dispersion method, the backward Lagrangian stochastic technique.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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