Issues and Options for Improved Freight Transportation in Costa Rica
Bibliographic record
Abstract
As international trade grows in Central America, it becomes more critical to move freight in an effective and efficient manner. Recent trade agreements between Central American countries and the rest of the world will likely increase the freight moved in the Central American region. Additionally, the expansion of the Panama Canal will result in increases in freight as larger vessels will be able to travel through the canal. To handle this potential freight growth and maintain their competitiveness, Central American countries must prepare their infrastructure and improve their operations. The road network is essential in moving freight to international border crossings and ports in Costa Rica; therefore, the reliability of the infrastructure and key connecting links are a primary concern for freight transportation. Port and border infrastructure and operations are also essential for the throughput of freight at the international level. Although international free trade agreements have the potential to increase freight, without corresponding improvements to the freight transportation system, the overall positive impact might be small. This paper investigates the issues and options associated with improved freight movement to, from, and through Costa Rica. The issues and options are addressed in the context of physical, operational, and regulatory constraints to freight mobility. The findings from this work raise issues that should be considered in the design, development, and implementation of a modern and efficient freight transportation infrastructure that can increase the economic competitiveness of Costa Rica relative to other countries in the region.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".