DETERMINATION METHODS OF FACTORS AFFECTING PIGGYBACK TRANSPORTATION
Bibliographic record
Abstract
The management of intermodal transportation is a complex and responsible process. Rail transport is traditionally used in the implementation of international and intercontinental piggyback transportation. The task of rail transport is to provide transportation over the so-called "land bridges" - land sections on which the route begins and ends, or through which it transits. Despite a significant level of computerization and informatization, the level of delays in the delivery of goods in the field of piggyback intermodal transportation is not decreasing. The unsatisfactory speed of piggyback trains is a significant factor in these delays. This problem is of common nature, and not faced by intermodal operators operating only in the Siberian and Eurasian continental land bridges, which pass through the territory of Russia and Kazakhstan, respectively, and deliver goods from Japan. It also applies to the American and Canadian land bridges, through which Japanese goods reach consumers in the United States and Canada, and through the ports of Germany and the Netherlands - to consumers in Western Europe. This situation has developed due to the lack of effective approaches to building management systems that would demonstrate a high level of efficiency in the face of uncertainty, which is a natural component of the transportation process. The article is devoted to the correlation analysis of factors affecting the cargo turnover of piggyback transportation in the Republic of Uzbekistan. The main factors affecting the cargo turnover of piggyback transportation were identified; the degree of the effect was established by statistical methods. Based on data obtained for the last ten years, a correlation matrix and a regression model of cargo turnover were built. The results obtained make it possible to build forecasts for the cargo turnover of piggyback transportation from two to four years with a 95% confidence interval. Keywords: multimodal transportation, factor analysis, regression model
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".