Development potential of logistics in the Arctic zone of the Russian Federation through the use of drones
Bibliographic record
Abstract
В статье рассматривается содержание транспортно-логистических задач в Арктике, а также перспективы международного сотрудничества. Изучен опыт других стран – Канады, США – в использовании дронов в логистике и доставке грузов. Раскрыта сущность использования беспилотных летательных аппаратов в логистике Арктической зоны, охарактеризованы преимущества и недостатки дронов на сегодняшний день и специфика их использования в Арктической зоне. Предложено использовать дроны в таких направлениях, как мониторинг состояния окружающей среды; контроль реальной загруженности логистических площадок на земле; транспортировки и перевалки грузов. Предлагается создать базу для оценки эффективности бюджета развития дронов для обеспечения логистических процессов в Арктической зоне РФ. Для этого проведено экономическое обоснование использования дронов в Арктической зоне в виде расчета чистого дисконтированного дохода и срока окупаемости проекта по использованию БПЛА в Арктической зоне. В качестве фактора экономии выступает меньшая потребность в топливе по сравнению с традиционными видами транспорта, используемыми в Арктике. Выделены проблемы и перспективы развития беспилотных перевозок в Арктической зоне. Сделан вывод о возможностях и изменениях развития в логистической деятельности в Арктической зоне в ближайшие годы. The article discusses the content of transport and logistics tasks in the Arctic, as well as the prospects for international cooperation. The experience of other countries – Canada, the United States – in the use of drones in logistics and cargo delivery was studied. The essence of the use of unmanned aerial vehicles in the logistics of the Arctic zone is revealed, the advantages and disadvantages of drones today and the specifics of their use in the Arctic zone are described. It is proposed to use drones in such areas as monitoring the state of the environment; monitoring the real workload of logistics sites on the ground; transportation and transshipment of goods. It is proposed to create a basis for evaluating the effectiveness of the budget for the development of drones to support logistics processes in the Arctic zone of the Russian Federation. For this purpose, an economic justification for the use of drones in the Arctic zone was carried out in the form of calculating the net discounted income and the payback period of the project for the use of UAVs in the Arctic zone. As a factor of economy, there is less need for fuel in comparison with traditional modes of transport used in the Arctic. The problems and prospects for the development of unmanned transportation in the Arctic zone are highlighted. The conclusion is made about the opportunities and changes in the development of logistics activities in the Arctic zone in the coming years.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".