Peculiarities of importing vehicles from the USA
Bibliographic record
Abstract
Introduction. In recent years, there is a tendency of growth in imports of vehicles. This situation is due to the formation of demand in the domestic market. Domestic enterprises are unable to meet this demand. The structure of imports is dominated by imports from EU countries (67.61%), but other markets such as the USA and countries of America (27.72%), North Korea (2.29%) and other countries are gaining new momentum. Imports of vehicles from the USA, Canada, Georgia and North Korea have now undergone certain logistical changes, because a large number of seaports are under occupation. This situation has caused a change in the route through the ports of the EU countries. Nevertheless, the prospects for expanding imports of vehicles from the USA are significant, because this market is represented by newer models, a wide range and nomenclature, cheaper segment, the availability of purchase due to the presence of a number of online auctions. The purpose of the paper is to form a detailed model of vehicle imports from the USA. The following methods were used in writing the paper: analysis, comparison, explanation, theoretical generalization, grouping, etc. Results. The paper analyzes the features of vehicle imports from the USA, presents the characteristics of the largest auctions representing used, damaged and new vehicles, describes how to participate in auctions, gives the structure of vehicle imports for 2021, identifies the prospects for vehicle imports. Also, in the publication there is a detailed model of import of vehicles from the USA, which provides a step-by-step description of import from the moment of searching at the auction to the moment of customs clearance in Ukraine, considering specifics of loading, delivery to the port of departure, choice of logistic method of sea transportation, insurance method, making changes in the supply chain by building new routes through the EU countries and customs clearance. Conclusions. The use of this model by a number of companies starting to import vehicles from the USA or planning to import, as well as having problems at certain stages of the import process, will allow to take into account all the nuances of this process and avoid mistakes. A detailed import model takes into account not only the selection of vehicles at auctions, but also transportation, insurance, shipping, and customs clearance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".