Oportunidad de negocio en el mercado chino para incrementar la exportación peruana de arándanos frescos de la región La Libertad
Bibliographic record
Abstract
ABSTRACT \nThis thesis report entitled "BUSINESS OPPORTUNITIES IN THE CHINESE MARKET FOR INCREASING THE PERUVIAN EXPORT OF LA LIBERTAD FRESH BLUEBERRY” has the purpose make know the business opportunity to companies and institutions that want to venture or diversify into a new market, knowing that the China market is highly attractive for the sale of blueberries. \n \nThis research project arises in view: the growing demand for blueberries and little competition in China regarding healthy products with high nutritional value, also found in blueberries as one of the crops with the greatest potential for the future, because this is a nontraditional fruit highly prized in the international markets due to its properties and characteristics. \n \nThe thesis highlighted that Peru has competitive advantages compared with other countries. Peru not only has the land and climate variability that is needed for the production of cranberries, but also has the ability to offer off-season, many markets like the U.S., CANADA CHILE and ARGENTINA SPAIN, so there is space for expansion. Additionally, it was made a market research about blueberries in China 2009-2013 in which the product got showed and analyzed, and tariff situation, requirements and \nbarriers. \n \nFinally, it was concluded that the business opportunities of the Chinese market will positively influence increasing Blueberries Peruvian exports in subsequent years.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".