Bibliographic record
Abstract
Vietnam is a country with strengths in agricultural production, rich and valuable agricultural products. Vietnam is often described as a giant boom with two big granaries at both ends, the Red River Delta and the Mekong Delta. Besides, the appropriate weather and climate conditions and fertile soil have created an ideal environment for rice production, thereby making rice a strong export item for Vietnam. In order to boost export rice output, the role of the transport and logistics system is very important. If the transport system is equipped and linked together, it will create added value for the components of the system, while reducing transport costs, warehousing costs, distribution, and circulation, ... increase farmers' income, profits for businesses, create great export value for the country. One of the most important tasks to achieve this goal is to optimize Vietnam's rice export system. This issue is always urgent, not only for the executive agency, macro-management of the State but also for organizations, businesses, ... involved in the production and export of rice. This paper focuses on analyzing and assessing the current situation of Vietnam's export rice transport system and forecasting the future in each period. From there, select the basic parameters (or criteria) to build the optimal export rice transport system. The author has built a general and specific model for 02 transshipment scenarios including scenario 1 is Saigon port, scenario 2 is Saigon port and Can Tho port. The core of the paper is a detailed calculation with 05 options for each scenario, based on the selected basic parameters, with the LINGO 13.0 FOR WINDOWS software. From there, identify and select the most optimal plan for Vietnam's export rice transport system in general and the Mekong Delta in particular.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".