Preliminary data on an affordable UAV system to survey for freshwater turtles: advantages and disadvantages of low-cost drones
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
Unmanned aerial vehicles (UAVs) are established, valuable tools for wildlife surveys in marine and terrestrial environments; however, they are seldom utilized in freshwater ecosystems. Therefore, baseline data on the use of UAVs in lotic environments are needed that balances flight parameters (e.g., altitude and noise level) with image quality, while minimizing disturbance to individuals. Moreover, the traditional high-cost UAVs may present challenges to researchers conducting rapid assessments on species presence with limited funding. However, emerging, affordable UAV systems can provide this preliminary data to researchers, albeit with caveats on reliability of data. We tested a low-cost UAV system to document freshwater turtle presence, species distribution, and habitat use in a small North Carolina wetland. We observed minimal instances of turtles fleeing basking sites (∼0.7%), as this UAV system was only ∼2.1 dB above ambient noise levels at an altitude of 20 m. Freshwater turtles were found primarily in algal mat basking habitats with highly variable numbers observed across locations and flights, likely due to image quality reliability and altitude. Our affordable UAV system was successful in providing baseline information on species presence, size distribution, and habitat preference of turtles in freshwater ecosystems.
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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 | 0.001 |
| 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