Mexican avocado subproducts destined for Canada
Bibliographic record
Abstract
The author of this thesis had doubts as to why most of the avocado hass and its sub-products business was monopolized by the United States. When talking to experts, he wondered why they did not choose to export to the Canadian market. That is why he focused on finding out the feasibility of exporting to Canada and discovered that they were shipping to this country, but not in a minimum percentage due to the people's lack of knowledge on how to get these products to the northern country. As a conclusion, the author tries to provide the knowledge of the requirements for exporters to expand their business. Based on the research conducted in the theoretical framework and surveys to experts in the field, analyze and obtain the necessary information to know the requirements for the logistics of Mexican avocado subproducts. These requirements will help businessmen in the logistics, export, and customs compliance they must consider. The objective is to conduct research that can help entrepreneurs dedicated to selling avocado guacamole subproducts locally or nationally who wish to expand their horizons to a potential Canadian market. The information obtained is put in a conclusion, explaining in question requirements such as phytosanitary certificates, transportation, documentation for crossing customs to reach their destination, and more issues to consider for those entrepreneurs who do not know the requirements or steps to be fulfilled to start exporting avocado subproducts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 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) | 0.023 | 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".