Bibliographic record
Abstract
Unmanned aerial systems (UAS), popularly known as drones, prove effective in many ways for biological research and wildlife conservation. “The benefits are true and real,” says David W. Johnston, executive director of Duke University's Marine Robotics and Remote Sensing Lab. As a marine ecology researcher, Johnston routinely employs UAS to study marine vertebrates. “Our results suggest we can reduce costs and produce better data than most of the occupied aircraft studies that we compare with, and with seemingly less disruption to the animals.” The systems each comprise an unmanned aerial vehicle (UAV), a ground-based operator, and a communication system linking the two. Johnston and the Marine Lab have flown UAVs for over 3 years, primarily over the ocean, improving their understanding and application of the technology. The use of unmanned surveillance reduces human risk while achieving the same, if not better, results. Publishing in Remote Sensing of Environment, University of Exeter scientists reported a structure-from-motion photogrammetry study of dryland ecosystem biomass that accurately measured plants just 15 millimeters high using a $3000 rotor drone with a mounted point-and-shoot camera. Similarly, Arizona State University's Enrique Vivoni has employed rotary and fixed-wing drones in multiple Sonoran and Chihuahuan rangeland studies to collect repeat high-resolution imagery and data much more cost-effectively. But there have been disappointments, too. Joseph D. Eyerman, RTI International's director of drone research and development, urges common sense when it comes to using drones. “Sometimes, the drone researchers, applications, and businesses are a bunch of people with a really neat hammer, and they’re all looking for nails,” he says. Last year, the nonprofit research group launched their drone program and created a standardized process to evaluate new technologies and applications. “In many cases, the drone is a good solution as it can add value to a project through low-cost, readily available, easy-to-use platforms—but it doesn’t necessarily solve the biggest challenges of a project.” Attempts by a handful of African government and conservation groups to use UAVs in the fight against wildlife poachers demonstrate that the gap between goals and reality has yet to be bridged, despite considerable time and money spent. As was reported in the New York Times in March, attempts to integrate UAS reconnaissance with enforcement by field personnel resulted in some poacher deterrence, a great deal of administrator frustration, and no prosecutions. Often, the poacher was long gone by the time field enforcers responded to the drone alerts. Eyerman points to the international development community as another example of malcontent. “There's a lot of resistance because they feel drones are being pushed on them,” he says. “During that one month of the year when their roads are too wet to drive, they’d rather use [their limited funding] for mosquito nets, not buy drones to maintain supply deliveries.” The Marine Lab and RTI spend considerable time on mission design and planning for that very reason. “It's important that you look at the entire environment where you’re going to use the aircraft and then make a decision on what's best for the research objectives,” says Eyerman, adding that RTI will not fly drones unless the mission design provides a cheaper solution. In Johnston's early missions, he found common UAV features—geospatial calibration and the return-to-home command—to be incompatible with seafaring Marine Lab missions. “Sometimes the technology is not always up to the task and gets pushed past its capability,” says Johnston. “The real value of drones will come into full force when we start using them to study people, their interactions with each other, and their environments,” he adds. “We need to establish expectations about being in public places and being observed.” However, there are no US federal standards governing the rights of people on the recording end of UAS. Last June, the Federal Aviation Administration issued its first set of operational rules for small, nonhobby aircraft, without addressing privacy concerns. According to the National Conference of State Legislatures’ unmanned aircraft website, a mixed bag of legislation exists at the state level, but only Indiana, Oregon, and Virginia have initial limitations on UAVs and human subjects. Eyerman says the increase in UAS will prompt changes soon, most immediately through the scientific community. “Standards on methodology, ethics, quality standards, quality-control processes, and results-publishing processes will be created—all the things that the mature sciences already have to protect and ensure quality and replicability.”
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.005 |
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".