A standardized protocol for reporting methods when using drones for wildlife research
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
Drones are increasingly popular tools for wildlife research, but it is important that the use of these tools does not overshadow reporting of methodological details required for evaluation of study designs. The diversity in drone platforms, sensors, and applications necessitates the reporting of specific details for replication, but there is little guidance available on how to detail drone use in peer-reviewed articles. Here, we present a standardized protocol to assist researchers in reporting of their drone use in wildlife research. The protocol is delivered in six sections: Project Overview; Drone System and Operation Details; Payload, Sensor, and Data Collection; Field Operation Details; Data Post-Processing; and Permits, Regulations, Training, and Logistics. Each section outlines the details that should be included, along with justifications for their inclusion. To facilitate ease of use, we have provided two example protocols, retroactively produced for published drone-based studies by the authors of this protocol. Our hopes are that the current version of this protocol should assist with the communication, dissemination, and adoption of drone technology for wildlife research and management.
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.
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 | 0.011 | 0.004 |
| 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