Bibliographic record
Abstract
Después de previamente analizar el estudio de Proexport acerca de la exportación de frutas exóticas, hemos decidido crear nuestra propia marca JUICE IT! de bebidas a base de fruta llamados Smoothies; este producto tendrá un equilibrio perfecto entre sabor, nutrición, conveniencia, variedad y valor. Nuestro target esta ubicado en Canadá especialmente en Toronto y ellos querrán encontrar todas esas características en los productos JUICE IT!. De acuerdo al estudio realizado por la embajada de Canadá, el consumo de frutas ha sido un éxito total y ha sido impulsado por las tendencias de comer cada vez mas saludable, además el consumo de bebidas a base de fruta congelada ha venido creciendo constantemente, ya que la calidad y la variedad de las frutas congeladas ha mejorado notablemente y el consumo de esta no esta limitada al deterioro de la misma. Queremos promover el concepto de vida saludable y el consumo de nuevos y ricos sabores exóticos, ya que la fruta va ser exportada de Colombia aprovechando las bondades en precios de exportación que nos proporciona el TLC entre Colombia y Canadá.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".