Knowledge of Arctic and EQQ Unmanned Aerial Vehicles for Multiple Applications
Notice bibliographique
Résumé
The rapid change in climate conditions, and the present demand for political and commercial interest in the Arctic region will cause considerable implications on the environment, ecosystem, security, and on the social system in the region. Today, governments, scientists and researchers understand that there is a huge gap of knowledge in the Arctic region and this must be addressed prior to development of the region, or there will be devastating environmental consequences in the future. Existing studies concluded by various organizations including Lloyd’s of London, US Geological Survey and other institutes emphasize that in order to ensure sustainable development in the Arctic, it is important to close the existing gap of knowledge by obtaining accurate scientific data, and make available this data to scientists, researchers and policy makers, for them to take sound decisions on both Arctic challenges and future economic opportunities. The scientists understand that existing lack of knowledge is mainly due to insufficient information in the Arctic and the inability to obtain sufficient scientific data to understand the Arctic region in-depth. Main challenges will be, the vast area of the Arctic, inaccessibility to complex and remote areas, long cold dark winters and short summers, rapid changes of weather conditions etc. Presently, existing satellites provide extremely valuable scientific data, however scientists emphasize that this data would be further analyzed (due to inaccuracy) and collaborated with data on actual close observations, physical sea–ice samples, ice core samples, data from surface and bottom of the sea-ice, glacial ice etc. Collecting data from high altitudes using Unmanned Aerial Vehicles are not new to the Arctic region, and have been used for number of years. The AMAP, (Arctic Monitoring and Assessment Program Workshop), Oslo October, 2008, recommended that it is of great importance that scientists use unmanned aerial vehicles in the Arctic to obtain important environment measurements. Further, added to the AMAP work plan for 2011–2013, is to develop safety guidelines and cross-jurisdictional flight pilot projects, to demonstrate the use of unmanned aerial service (UAS) in the Arctic Environmental Monitoring Plan. The Canadian Government also completed the feasibility study to build a “High Arctic Research Station” in the high north to serve the entire world, for scientists to have an opportunity to share data and support the knowledge for researchers to conclude their investigations. The government is further considering purchasing, three large high-altitude Global Hawk drones for Arctic surveillance, and seeking small snowmobiles and remote control aircrafts to monitor the extreme complex landscape of the Arctic. At present, there is no method to obtain accurate surface and atmospheric data in complex and remote areas, and this requirement has become the highest priority and should be addressed urgently. In order to obtain sufficient accurate data from the Arctic surface and atmosphere, EQQUERA Inc. innovated, is designing and developing multipurpose, multifunctional SG EQQ Unmanned Aerial Vehicles that are able to access remote and complex areas in the Arctic, and operate in challengeable weather conditions such as cold long dark nights.
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 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,016 | 0,010 |
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 ».