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Record W4244059214 · doi:10.1525/bio.2013.63.9.18

Drone Ecology

2013· article· en· W4244059214 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueBioScience · 2013
Typearticle
Languageen
FieldEngineering
TopicUAV Applications and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsEcologyGeographyDroneBiology

Abstract

fetched live from OpenAlex

Steep coastal cliffs, foggy weather, and smoke-filled skies can impede wildlife monitoring and endanger the lives of researchers working in remote areas. Field biologists are now exploring the utility of harnessing unmanned aircraft systems (UASs)—or drones—as a scientific tool. “This technology is going to be revolutionary for a lot of [ecology] related fields,” says Adam Watts, assistant research professor at Nevada's Desert Research Institute. In 2008, US Geological Survey (USGS) scientists began testing the utility of fixed-wing and hovering aircraft for a variety of missions, such as locating pygmy rabbit burrows in sagebrush, surveying sandhill cranes, and monitoring forest insect infestations. For the pygmy rabbits, which are endangered in Washington State, drones could prove to be a major time saver for closely examining the tiny rabbit's habitat and food preferences. Biologists have had to painstakingly pick plant leaves and take them to the lab to study chemical signatures. A USGS drone called the Raven can carry cameras capable of infrared spectrometry, a technique that may provide spectral chemical sensing of vegetation from the air, which would vastly speed up access to this type of data. The US Forest Service and university researchers are also testing drones. Watts used UASs to count manatees, wading birds, and sage grouse nests, and to survey vegetation around Lake Okeechobee while at the University of Florida. And with a $5 million award from Google, the World Wildlife Fund is testing the deployment of drones to monitor wildlife and to combat the poaching of endangered rhinos, tigers, and elephants. The still-evolving systems may eventually help track down wildlife criminals while reducing personnel time on the ground and providing a deterrent by keeping a watchful eye along roads and fences. Small UASs can mean large cost savings. The average weekly rate for flying a small UAS with a ground crew is $3000, explains Mike Hutt, who leads the USGS National UAS Project Office, compared with $30,000–$50,000 for similar missions with manned helicopters. With electric engines, small UASs also have smaller ecological footprints than petroleum-fueled manned aircraft do, and their quiet engines are less disruptive to wildlife. Hutt's original area of expertise is remote sensing, and, as he explains, UAS data have another clear advantage over satellite data. To monitor Colorado's pine beetle infestation, the USGS relies on Landsat satellite imagery, which flies over the area during summer afternoons, when thunderstorms build up. “So we would get a lot of images with a lot of clouds. It would take months to get a mosaic of images that would allow us to look [in detail] at an area,” he says. Monitoring the forest with a small drone is a huge advantage, explains Hutt, who thinks the technology will supplement, rather than replace, traditional aircraft and satellite observations. How difficult is it to fly a UAS? It depends on the size and complexity of the aircraft, explains Dominique Chabot, a PhD student at McGill University who's studying the utility of UASs, with David Bird, a professor of wildlife biology. A self-described “technogeek,” Chabot focuses on off-the-shelf electric drones. Originally designed for crop surveillance, these UASs resemble radio-controlled hobby airplanes. “There's a steep learning curve,” says Chabot. Although aircraft have a preprogrammed autopilot, the user must pilot manually if the aircraft gets into trouble. Chabot spent dozens of hours practicing with a flight simulator. Nevertheless, technical issues are not the greatest challenge to using UASs, say researchers. The restrictive and sluggish permitting processes required by Transport Canada and the US Federal Aviation Administration (FAA) stifle the tools' utility for scientific applications, making ad hoc, time-sensitive flights difficult to arrange. The other challenge, says Bird, is to convince the public “that these planes are not going to be used for terrible things.” Congress has ordered the FAA to overhaul regulations and to open the skies to private drones by 2015, but this will involve navigating what the Washington Post describes as “a patchwork of state regulations.” Perhaps the most important advantage of UASs is the potential to save human lives. “The greatest source of [accidental] mortality to wildlife biologists is dying in a plane or helicopter crash,” says Bird. He had a close call himself in a helicopter and references recent tragedies in Alberta, California, and Idaho, in which biologists and pilots were killed. Bird, founding editor of the new Journal of Unmanned Vehicle Systems, launched by Canada's National Research Council Press, says that his veer toward drone research was almost accidental. Contacted by farmers wanting him to design a UAS to scare away crop pests such as starlings, Bird says that his “mind exploded” with ideas for using drones to study wildlife. What's next? This fall, Bird is heading a team of UAS experts to track the movements of threatened woodland caribou bearing satellite transmitters in Goose Bay, Labrador, Canada. For Bird and the other scientists who see their potential, drones are like a child's new toy: not yet fully understood but promising excitement and a world of possibilities.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.650
Threshold uncertainty score0.630

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.003
GPT teacher head0.156
Teacher spread0.153 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it