Knowledge of Arctic and EQQ Unmanned Aerial Vehicles for Multiple Applications
Bibliographic record
Abstract
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.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.010 |
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".