Generalized flux-gradient technique pairing line-average concentrations on vertically separated paths
Bibliographic record
Abstract
Line-averaging optical gas detectors offer new avenues for the indirect estimation of surface/air exchange fluxes. This paper examines an inverse dispersion technique (gFG, for “generalized flux-gradient”) that yields an estimate of the gas emission rate Q from surface area sources, based on the difference ΔC between line-averaged mean concentrations along two (or more) paths that are vertically inclined or, if horizontal, are vertically separated. The inversion to extract Q from ΔC can be performed using any satisfactory model of turbulent dispersion over a finite source, motivating the examination here of several analytical solutions to the advection-diffusion equation. Each provides a theoretical value u*ΔC/Q for the normalized concentration difference, whence an estimate Q˜ of the flux can be deduced from measured ΔC and u* (the latter being the friction velocity, for which any suitable velocity scale could be substituted). Discrepancies between the solutions are explored, and the error that results from wrongly treating the source fetch as infinite is quantified. As the fetch increases, gFG relaxes to the standard flux-gradient technique exploiting the (known) Monin–Obukhov concentration gradient.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".