Integrating Natural Resources Education and Citizen Science Communication through the Use of Unmanned Aerial Systems (Drones)
Bibliographic record
Abstract
Science communication is increasing through the use of Unmanned Aerial Systems (UAS) or drones. Within the Arthur Temple College of Forestry and Agriculture at Stephen F. Austin State University (SFASU), UASs such as the DJI Phantom 4 Pro and Mavic Mini2 drones were used by students and faculty to study mistletoe, crapemyrtle and fire ants and then drone images were uploaded to iNaturalist, the largest repository for flora and fauna specimens to share with the scientific community and general public. The benefits of using a UAS is that nadir (directly above) images of the specimens increase the locational accuracy of each specimen compared to distance images acquired from a smartphone. By incorporating drones into course works at SFASU, faculty are increasing the technological abilities of students to communicate natural resource information to a greater audience as a citizen scientist. With ever increasing capabilities and lower cost, UAS are becoming a viable alternative to smartphones for communication of science, especially for iNaturalist. The ability to communicate science information and display images adds a dimension for the citizen scientist to use a UAS in teaching and information exchange while creating a well-rounded, better informed, and more employable student upon graduation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".