Analysis of importing process of Canadian Ice Wine to Finland
Bibliographic record
Abstract
The thesis was made for the needs of a company planning to import dessert wine called ice wine from Canada. The objectives of the thesis were to research the steps of the importing process, the laws, formalities, prerequisites, and stakeholders related to importing alcohol to Finland. In addition, the objectives were to find out what kinds of challenges and problems a wine importer might face and what kinds of sales and distribution channel alternatives there are for wine in Finland. Since the company has no prior importing experience, the purpose of the thesis was to define importing process related issues the company should be prepared for. The results can be useful also for other small companies interested in wine import. \n \nThe thesis was based on secondary research and analysis of that information for the company’s needs and on one interview with a Finnish wine importer. Literature review was conducted by studying information related to the importing process from an operative point of view. \n \nThe main results and the conclusions of the thesis are that it is very important to have a sufficient cargo insurance due to long distance and possible theft. For a small company the use of freight forwarders for transportation arrangements and customs declarations is essential since they have the expertise and network needed when importing from outside the European Union. A wine importer can sell and distribute wine in Finland through the retailing monopoly Alko Oy, or via restaurants and bars. The distribution is most efficient when it takes place either from the importer’s own or subcontracted warehouse.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".