Approach to a business plan to export colombian gooseberry to Canadá
Bibliographic record
Abstract
In recent years, no export opportunities have been taken for different fruits and important spaces have been lost in the national market where consumption of national and imported fruits and vegetables has been changed. Since the 80s when it was proposed to diversify exports of non-traditional products. From this moment the Cape Gooseberry positioned itself as a leading product in Colombia's exports. In the course of the specialization, knowledge has been acquired regarding foreign trade (exports and imports), in addition they gave us development tools that seek to establish and create a business opportunity which is the purpose of this project, to establish a business idea feasible for the realization of this in addition to the creation of a cape gooseberry exporting company. The Colombian gooseberry exporting plan in Canada will take advantage of the fact that this fruit is affected as exotic, apart from its benefits it is attractive to the foreign market. On the other hand, it considers an innovative project since it promotes the development of the beneficiary field to the producers. In this degree work, studies are carried out to demonstrate and analyze the viability of the project in terms of marketing, logistics and finance. The studies finally yielded positive results and finally the export of Colombian Cape Gooseberry to Canada will be taken as a great business opportunity.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".