Supply Chain Control and Strategies to Reduce Operational Risk in Russian Extractive Industries Along the Northern Sea Route
Bibliographic record
Abstract
Russian resource developers operating in remote parts of the Arctic have demonstrated over the past several years that it is feasible to extract natural resources throughout the year, and ship large quantities of raw materials with regular intervals from the Arctic to international markets; this despite very difficult operational conditions in the Arctic during both winter and spring. Several resource extraction projects are currently being implemented or planned. This study examines how the extractive companies have built up enhanced supply chain resilience and transport reliability to mitigate common Arctic risks. The companies have taken control over supply chains and adopted several precautionary and innovative infrastructure and logistics measures designed to prevent or mitigate disruption to these supply chains. Preferred logistical solutions for all of these extraction projects have developed into large package deals, where long-term production and transport of commodities, icebreaking services, and state support are all included. Western sanctions on Russia as a result of the war in Ukraine, will slow down the pace of future Russian projects in the Arctic, at least in the short to medium-term, but the sanctions are likely to increase the future significance of export terminals on the NSR, as the preferred departure points for Russian Arctic commodities on their way to selective market destinations.
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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 | 0.001 | 0.002 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".