Electronic Atlas of Climatic Changes in the Western Russian Arctic in 1950–2021 as Geoinformatic Support of Railway Development
Bibliographic record
Abstract
The Arctic zone of the Russian Federation is one of the most intensively developing regions of the country. Amongst the major domains of economic and industrial growth and improvement is transport infrastructure and particularly the railway network. This area is being exposed to negative factors of rapid climate change that can significantly affect and compromise this activity. Thus, it is vital to take them into account during design, construction, and operation of the railway infrastructure facilities. This work details the production of a digital atlas comprising the 1950–2021 dynamics of the main hydrometeorological parameters: air and soil temperature, precipitation, wind speed, air and soil humidity, and snow cover thickness. The maps are based on climatic data derived from the MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, version 2) reanalysis. In total there are 459, which are arranged into 7 chapters. The atlas geographically covers the western part of the Russian Arctic encompassing the regions of quite intensive transport development, which includes the construction of the Northern Latitudinal Railway. Original algorithms of geospatial data processing and their further representation as well as the maps compiled in GIS environment are discussed. Comprehensive analysis of climatic changes in the region of the Russian Arctic including detailed quantitative evaluation over 40 years is given. In the Discussion, we focus on those changes of the regional climate which, from our point of view, are the most significant for consideration by railway operators. The obtained results contribute to framing the theoretical basis of design, development, and sustainable operation of the railway infrastructure in the Arctic and facilitate the decision-making process. This is the first experience of building a specialized climatic cartographic product for the needs of the Russian railways, and to our knowledge the first atlas such as that in the world. In the future, the amassed experience may be transferred to other regions of the Russian Federation as well as similar regions in Canada, Sweden and Highland China that are also subject to significant climate change.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".