The DeLeaves: a UAV device for efficient tree canopy sampling
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
Tree canopy sampling is critical in many forestry-related applications, including ecophysiology, foliar nutrient diagnostics, remote sensing model development, genetic analysis, and biodiversity monitoring and conservation. Many of these applications require foliage samples that have been exposed to full sunlight. Unfortunately, current sampling techniques are severely limited in cases where site topography (e.g., rivers, cliffs, canyons) or tree height (i.e., branches located above 10 m) make it time-consuming, expensive, and possibly hazardous to collect samples. This paper reviews the recent developments related to unmanned aerial vehicle (UAV) based tree sampling and presents the DeLeaves tool, a new device that can be installed under a small UAV to efficiently sample small branches in the uppermost canopy (i.e., <25 mm stem diameter, <500 g total weight, any orientation). Four different sampling campaigns using the DeLeaves tool are presented to illustrate its real-life use in various environments. So far, the DeLeaves tool has been able to collect more than 250 samples from over 20 different species with an average sampling time of 6 min. These results demonstrate the potential of UAV-based tree sampling to greatly enhance key tasks in forestry, botany, and ecology.
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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 | 0.001 | 0.000 |
| 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 it