Optimal settings and advantages of drones as a tool for canopy arthropod collection
Bibliographic record
Abstract
The growing field of aeroecology is limited by difficulties associated with sampling in the air column. Aerial insects are particularly hard to sample, despite being the main prey in the air column, with some recent studies attempting to use drones as a collection method. We conducted a study to determine the optimal drone settings for collecting insects above the canopy, where drones are seldom used. By attaching a net to the body of a small, commercial drone, we tested yield from different height, speed, and net settings in wetlands, as well as compared insect diversity across different habitat canopies. Height was the most important setting; grazing the canopy yielded significantly more insects than flying one meter above it. Speed, drone type, and net size did not influence the number of insects caught per trial. Wetland canopies had higher abundance, diversity, and species richness in its arthropod populations compared to forest canopies or lakes. Compared to the yield of Lindgren funnels-a traditional sampling method in entomology-drones captured higher diversity and abundance of insects in a fraction of the time. This study confirms that drones are an efficient and accurate way to collect canopy arthropods.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".