Medidas técnicas que limitan la exportación de cebolla amarilla dulce fresca en Shuman Produce Perú S.A.C. a Colombia, 2018
Bibliographic record
Abstract
ABSTRACT \n \n \nShuman Produce Perú S.A.C. it is a company dedicated to the export since 2006 to the production and export of fresh onion to 15 destination markets such as: United States, Canada, Spain, Peru, Holland, Guatemala, among others. The company began its exports of this vegetable in 2014 by land and sea from 2016 to 2018. \n \nThe fate of fresh onion is made up of countries in Europe, North America and Asia, where it originated. Its production period is in the months of April to June and the harvest is in the months of August to November. In relation to fresh sweet yellow onion, the highest production is in Ica with a participation of 12.8% nationally and provincially, 87.9%. \n \nThe objective of this research is to analyze the technical measures that limit the export of fresh sweet yellow onion in Shuman Produce Perú S.A.C. to Colombia, 2018. \nIt was concluded that the technical measures that limit the export of fresh sweet yellow onion in Shuman Produce Peru S.A.C. Colombia is: Sanitary and Phytosanitary Measures, Technical Barriers to Trade and Pre-Shipment Inspection. \n \nFollowing the results obtained, recommendations focused on the exporting companies of fresh sweet yellow onion were prepared, in order to have more information so that in the future they know how to face this restriction imposed by Colombia on exports of this vegetable.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".