Optimization and Representativeness of Atmospheric Chemical Sampling by Hovering Unmanned Aerial Vehicles Over Tropical Forests
Bibliographic record
Abstract
Abstract Atmospheric chemical species play critical roles in ecosystem functioning and climate, but spatially resolving near‐surface concentrations has been challenging. In this regard, hovering unmanned aerial vehicles (UAVs) represent an emerging technology. The study herein provides guidance for optimized atmospheric sampling by hovering copter‐type UAVs. Large‐eddy simulations are conducted for species having chemical lifetimes ranging from reactive (i.e., 102s) to long‐lived (i.e., 108s). The case study of fair‐weather conditions over an equatorial tropical forest is used because of previous UAV deployments in this region. A framework is developed of influence length and horizontal shift of upwind surface emissions. The framework quantifies the length scale of the contribution of upwind forest emissions to species concentrations sampled by the downwind hovering UAV. Main findings include the following: (1) sampling within an altitude that is no more than 200 m above the canopy is recommended for both high‐ and intermediate‐reactivity species because of the strong decrease in species concentration even in a highly turbulent atmosphere; (2) sampling durations of at least 5 and 10 min are recommended for intermediate‐ and high‐reactivity species, respectively, because of the effects of atmospheric turbulence; and (3) in the case of heterogeneity of emissions across the underlying landscape, maximum recommended altitudes are presented for horizontal sampling strategies that can resolve the variability in the landscape emissions. The coupled effects of emission rate, wind speed, species lifetime, turbulence, and UAV sampling duration on influence length must all be considered for optimized and representative sampling over forests.
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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 | 0.002 |
| 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) | 0.000 | 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".