El mercado canadiense como oportunidad de negocio para la exportación de la bebida Chía Power de la empresa Agroensancha S. R. L. de la ciudad de Trujillo – La Libertad, 2016
Bibliographic record
Abstract
ABSTRACT \nThis following research work was carried out with the objective of analyzing the business \nopportunity to export to Canada the product called "Chia Power", a blueberry-flavored chia \nbeverage, which is an innovative and attractive product for foreign markets because \nPossesses nutritional qualities that benefit health care, a trend very marked today. The \ncompany Agroensancha is the manufacturer of this product; However, it does not have the \nnecessary advice or sufficient knowledge to penetrate its presence in international markets. \nTherefore, using a fundamental tool, such as market research, serves to identify the \nbehavior and customs of the consumer, competition in the sector, tariff and non-tariff \nbarriers and consumption trends, which will help us to adapt and improve the product so \nthat the entrance to the Canadian market is a success creating greater income for the \ncompany. \nThe research aims to analyze the business opportunity in the Canadian market for the \nexport of the Chia Power Drink blueberry flavor of the company Agroensancha SRL of the \ncity of Trujillo - La Libertad, here they establish their delimitations, justification and \nobjectives that are important To guide the investigation. \nIn the development of the market study the seasonality of inputs, tariff and non-tariff barriers, \ndemography, consumer behavior, distribution channels, competition, demand, Peruvian \nexports and imports were analyzed to make the appropriate proposal that contributes to the \nopening of Market of the company Agroensancha. \nFinally, the present research concludes that there is a great business opportunity of the \nproduct in Canada because it is a country where the majority of the population is adult, this \nsegment focuses on acquiring innovative products that contribute to health care. The \nproposal focuses on covering the majority of consumers by developing the presentation for \nadults, which consists of a 500 ml PET bottle. The purpose of this strategy is to enter this \nmarket showing the nutritional and functional benefits offered by Chia drink with blueberries \ncalled "Chia Power".
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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 | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".