Features of auto-commodity expertise of vehicles imported from the USA and Canada
Bibliographic record
Abstract
The article analyzes the world and domestic car market, including the dynamics of world car sales, the number of cars manufactured per 1000 inhabitants in particular countries, characterizes secondary car market in Ukraine as well as its main trends. The procedure for the customs value of a customs-uncleared and unregistered in Ukraine car imported from the USA has been determined, including the obligatory information about the car; the market value of road vehicles imported into the customs territory of Ukraine is calculated using the direct comparison method, the factors of increase and decrease of the customs value of the car are listed. The criteria for identifying cars in carrying out auto-technical and auto-commodity expert examination are established; the information that can be obtained as a result of VIN-code verification is provided as well as the sources for verifying the VIN-code info. Theoretical generalizations on the research problem are made; the identification criteria in the performance of auto-technical and auto-commodity expertise are systematized; new criteria for imported vehicles price confirmation and determination have been developed; the methodical tools of determining the customs value of customs-uncleared and unregistered in Ukraine car imported from the USA have been improved. New criteria for the price confirmation and determination of imported from the USA and Canada vehicles, as well as the identification criteria for the implementation of autotechnical and auto-commodity expertise during the crossing of the customs border have been practically applied.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".