Digital and Logistical Infrastructures of the Arctic Zone: Current State of Research and Ways of Development
Bibliographic record
Abstract
The subject of the article is the analysis of the current state of research and practice in the field of transport, logistics and digital infrastructures in the Russian Arctic. The authors consider logistic and digital infrastructures as key communication subsystems that ensure the movement of material values, people and information, and thus serve as a prerequisite for the development of the macro-region under consid-eration. The research methods used were literature review of scientific sources and analysis of the ob-tained material. A systematic literature mapping was carried out on Scopus, Google Scholar, Elibrary data-bases. Aspects of the Arctic region development, including the role of logistics and digital infrastructures in this process, are described by scientific schools of Russia, Canada, Norway, China, USA and other countries. The analysis has shown the highly variable state of the subsystems under consideration depending on the specific region, as well as the lack of a comprehensive approach to their joint development and integration. It was stated that there is no such problem statement about the development of communication subsystems of the Arctic zone. On the basis of the analysis, the key directions for the development of logistics and digital infrastructure of the Arctic zone of the Russian Federation were formulated. The authors’ recommendations, apart from the obvious tasks of developing sea routes, building and modernizing roads, railways and airports, expanding access to broadband internet and other widely discussed measures, describe the need to integrate the communication subsystems under consideration and focus on the potential of digital technologies to replace and/or supplement the logistics infrastructure in certain aspects.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".