Notice bibliographique
Résumé
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.”
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,031 | 0,005 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».