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Funneling the UAS Flock

2017· article· en· W3173933806 sur OpenAlexaboutno aff
Kaylee Cusack

Notice bibliographique

RevueUND Scholarly Commons (University of North Dakota) · 2017
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueSocial Movements and Cultural Identity
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésFlockGeographyBiologyEcology
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Growing number of UAS-related companies use UND Center for Innovation for coaching and collaboration In 2014, UND history graduate student Matt Dunlevy was working toward the completion of his thesis. But even though his academic focus was on the past, his business brain was pulling him to the future—of the commercial drone industry. “I recognized that this was something I could be passionate about, so I decided to jump in head first. The rest is a short history, but still history,” Dunlevy recalls. That October, Dunlevy started SkySkopes, an unmanned aerial systems (UAS) company of which he now serves as president and CEO. His initial value proposition was making money by flying small photography drones at weddings. “We could charge something like $20 a minute of flight time and hand that off to a wedding planner or a groom or bride, making that wedding that much more special,” Dunlevy said. “But the problem is, that’s just scratching the surface of the true value of unmanned aircraft, which we’ve learned day-in and day-out since then.” Those lessons—which have allowed Dunlevy to take on 10 full-time employees and continually expand his industry-leading inspection and photography services—have come by way of SkySkopes’ inclusion in a band of more than two dozen UAS-related companies connected through UND’s Center for Innovation. Center Director and Entrepreneur Coach Bruce Gjovig says these ventures are being drawn to Grand Forks because of a perfect mixture of factors, including Grand Forks Air Force Base drone operations, UND’s UAS degree program, and the Federal Aviation Administration (FAA) designation of Grand Forks as a UAS test site for the industry—making it one of seven test sites that will help integrate the commercial applications of UAS into the national airspace. “Besides the 26 companies we have as a part of the UAS entrepreneur cluster, there are another 18 companies who have shown an interest,” Gjovig said. “Already we have companies from not only around the United States, but also from Finland, Norway, Israel, the Czech Republic and Canada—all interested in being here. But there’s a lot more that will come as the airspace gets opened up.” The flock of UAS ventures to Grand Forks is also driven by the fact that two major industry users are here in the state—energy and agriculture. UAS is becoming more integral for powerline and pipeline inspection and for checking crops for weeds, insects and disease. “People are looking to the UAS and drone industry as the most important new industry that’s going to bring new technology and new jobs to North Dakota,” Gjovig said, adding that UAS is essential in diversifying the state’s economy. Cluster collaboration With the guidance of SkySkopes CFO Dan Daffinrud, the Center applied for and received a $50,000 Small Business Administration (SBA) Accelerator Award last summer to help form “Autonomous Alley”—the nation’s first UAS entrepreneur cluster. “It’s taking the services that the Center for Innovation currently offers and tailoring it for the UAS market,” Daffinrud said, adding that those services include entrepreneur coaching and access to capital. “Autonomous Alley recognizes that we have an opportunity here to take an early lead in creating an ecosystem for UAS and recognizing that this is a long-term growth opportunity for Grand Forks and the state.” Gjovig said many UAS-related companies are startups that are not only looking for venture development assistance, but also a place to share resources and expertise with others in the industry. “In unmanned aerial systems, the most important word is systems,” he said. “Typically, somebody is very good at something—it could be the platform, it could be flying, it could be the different kind of sensors, data collection or analytics…” But, as Gjovig continued, people are usually not experts in every area. “They need to be around others who are experts so they can work together and find ways in which they can integrate and work toward a solution for the customer,” he said. The crew at SkySkopes has seen this ability to collaborate bolster their success as a company, and as a collective. “We can partner with other participants in this cluster to complete the value chain so that we can go out and be more competitive against the rest of the national and international market,” Daffinrud said. “That’s really helped prop up this cluster and bring a spark of innovation and opportunity, just because we share a hallway.” Driver of success The Center for Innovation was recently awarded the 2017 Dinah Adkins Technology Incubator of the Year Award from the International Business Innovation Association, which took special interest in the Center’s UAS cluster. “This incubator certainly deserves that honor. It goes to show the innovative spirit and the entrepreneurial drive of Bruce Gjovig and his staff,” Dunlevy said. And that drive is what has propelled SkySkopes and others on their flight plans of growth. “Because of the man who is responsible for this cluster, and the coaching and the mentorship from the leadership of other UAS companies here, we know more about what’s going on in the industry in general, we know what to expect for the future of UAS and we know where the end users are,” Dunlevy said.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,003
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Autre · Signal consensuel: Autre
Score de désaccord entre enseignants0,055
Score d'incertitude au seuil0,184

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,003
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0020,001
Études des sciences et des technologies0,0030,001
Communication savante0,0040,005
Science ouverte0,0010,006
Intégrité de la recherche0,0020,002
Charge utile insuffisante (le modèle a refusé de juger)0,0550,022

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.

Tête enseignante Opus0,052
Tête enseignante GPT0,294
Écart entre enseignants0,242 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2017
Routes d'admission1
Résumé présentoui

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