Preliminary data on an affordable UAV system to survey for freshwater turtles: advantages and disadvantages of low-cost drones
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.003 | 0.001 |
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".