Potencialidades de exportación de carne de bovino de Colombia, en el marco de un posible tratado de libre comercio con Japón
Bibliographic record
Abstract
Este proyecto se enfoca hacia la posibilidad de exportar carne de bovino colombiana a Japon, en el marco de un posible tratado de libre comercio. Japon, es un pais asiatico que posee alta demanda de la misma, sin embargo, su falta de superficie le impide la produccion de carne bovina, volviendose uno de los mayores importadores (puesto 5) a nivel mundial de este producto. En vista de lo anterior, nace la oportunidad para Colombia de convertirse en un viable exportador de carne bovina hacia el pais asiatico. Todo esto se debe, a que el pais cuenta con ventajas como la superficie y el clima, llevandolo a convertirse en un pais agricola con alto potencial y capacidad de produccion de carne de bovino. Al realizar el estudio pertinente, se considera que los mayores exportadores del producto hacia Japon son: Australia, Estados Unidos, Nueva Zelandia, Canada y Mexico, siendo Australia y Estados Unidos los mayores proveedores de Japon. Por ello, se evaluaron estos paises como los principales competidores de Colombia, demostrando altas diferencias, dado que Colombia no cuenta con los requerimientos para exportar carne de bovino hacia Japon, por deficiencias en calidad, precio y cantidad.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".