Transformation of transport arteries of Russia within the paradigm of green economy in the context of forestry
Bibliographic record
Abstract
Abstract The aim of the article is to find effective growth points for the Russian economy, identify problems hindering the sustainable development according to the green paradigm. The benefits of Russia’s participation in international logistic project of recreating the new Silk Road are researched for forestry industry. Positive dynamics of the forest industry was achieved in 2018: in 10 months of 2018, the industrial production index in the woodworking sector was 109%, in the pulp and paper industry – 113%. Despite the positive trends, the capacity of the industry is far from fully revealed. The share of Russia in world forest turnover is 3% of the global volume unlike such countries as Finland (8%), Sweden (10%), the USA (13%), Canada (17%). The main consumers are China (76%), the Republic of Korea (20%), Japan (4%). Processed timber is acquired by China (83%), the Republic of Korea and Japan (17%). The development of export-oriented woodworking enterprises with high-value-added products in Russia demands a significant transformation of the transport-logistics complex. The article researches the main limitations of the transport system (low density of roads, the high demanding infrastructure investments, high costs of transportation), which do not allow enterprises to work effectively in foreign markets.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".