The application of the ARCH model for the assessment of transport routes in Northern Europe and Southeast Asia
Bibliographic record
Abstract
Abstract The article proposes the method of comparison of future transport routes to connect North Europe and Southeast Asia. Two transport routes can pass through the expanses of the Arctic Ocean, along the shores of Russia and Canada. The Southern Sea Route on the Suez Canal and the Trans-Siberian Railway are the alternative transport routes. The Northern Sea Route is the shortest waterway between Northern Europe and Southeast Asia and has many advantages of goods transportation in the future. Compared to the Northwest Route along the Canadian coast, the Northern Sea Route has a greater number of competitive advantages. The authors outline the stages of transport routes comparison method. At the first stage, the goal of the analysis is set and the mathematical model is chosen. The second stage determines the resulting and influential indicators. The third stage involves collecting the information on selected indicators with each indicator being a time series. At the fourth stage, time invariance of each time series is analyzed. At the fifth stage, model is selected that displays the transport corridor. The sixth and seventh stages deal with autocorrelation and multicollinear analysis. At the eighth stage the coefficients of the equations are calculated. At the ninth stage, the conditional variance of the series of the resulting indicator is estimated and the decision is made whether to use the model of autoregressive conditional heteroscedasticity (Arch – Auto Regressive Conditional Heteroscedasticit) for prediction. The paper describes the results of comparison of various international transport routes.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".