Sensitivity of Lagrangian Stochastic footprints to turbulence statistics
Bibliographic record
Abstract
This study tests the sensitivity of a Lagrangian Stochastic footprint model to the turbulence statistics describing the flow field, with a focus on the within canopy processes. Representative profiles of the input velocity statistics are taken from a long-term dataset of turbulence measurements within and above a tall spruce canopy. Based on a wavelet tool, which allows a detailed analysis of coherent structures along the vertical profile, we characterize several typical states of coupling and decoupling between surface, canopy and atmosphere. For each coupling regime, three flux footprints using different sources for turbulence statistics are compared: the first based on conditionally-averaged measurements, the second on a simple numerical solution and the third on measurements taken from literature. The effects of profile smoothing and connecting measured canopy data to parametrized atmospheric surface layer profiles are considered. Significant differences between footprints based on modelled and measured profiles were found for exchange regimes with the lower section of the profiles decoupled from the atmospheric surface layer. As such cases are likely to occur for tall canopies with moderate density, our results suggest that the accuracy of Lagrangian Stochastic footprint modelling could be improved by using better turbulence profiles for different exchange regimes.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".