Capacidad de Producción Exportadora de la Tilapia Roja en el departamento del Huila, Colombia
Bibliographic record
Abstract
This article shows the production capacity for export of Red Tilapia (also called mojarra) from the department of Huila in Colombia. This export has been given thanks to the competitive and comparative advantages of the region, which maximize the use of its cultivation and that can be summarized in the water wealth of the Bethania Dam that generates excellent streams of water and oxygen from the main Colombian river the Magdalena and that have in its favor an ideal ambient temperature for rapid growth in captivity of the mojarra, fish highly resistant to stress and therefore resistant to diseases. These advantages have allowed the department of Huila to become the first national producer of red tilapia and the main fillet exporter to countries such as the United States, Peru and Canada and expand its prospects for exporting to more Latin American countries, the European Union and even Get to Japan. In 2018, Colombia exported 5,312 tons of red tilapia fillet to the North American market for a value exceeding US $ 47.9 million, increasing exports by 32% compared to the same period of the previous year, of which Huila participated with 99% of national production and generated US $ 42.1 million. Thanks to this vertiginous growth, the department has seen the need to create internal agendas to increase aquaculture production and respond to the great existing business opportunities with this product, since the cultivation of tilapia in Huila has generated an important strategy of inclusion as it has involved special populations to the economy and sustainable social development.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".