Freight flows, logistics costs and efficiency : optimal path analysis - background paper
Bibliographic record
Abstract
In Central America, cargo is transported almost entirely by road. The movement of imports and exports to and from international seaports is done by truck. Rail service is almost nonexistent and air transport serves less than one percent of the cargo generated within the Central American Common Market (SIECA, 2004). Intra-regional trade is much more important in Central America than it might seem at first glance. The second largest trading partner of Central America is the region itself. In 2010, one quarter of the exports from Central America were destined for final consumption within the region. Half of the exports of Central America (54 percent in 2010) correspond to agricultural products and a large proportion of them supply markets inside the region. Nearly 40 percent of intra-regional exports consist of food, beverages, animals and plants (SIECA, 2011). Perishable food products are transported on trucks, and spatially restricted by the geography and the road infrastructure. In this context, inefficiencies in the supply chain and delays in freight flows lead to economic losses and amplify the negative impact of the distance to the markets on trade. A gravity model of trade showed that the negative effect of distance1 on total intra-regional exports is 77 percent higher in Central America than in the European Union (World Bank, 2010). More precisely, an increase in distance by 1 percent is expected to reduce intra-regional bilateral exports in Central America by 1.65 percent. In terms of volume, the negative effect of distance within the region exceeds the effect in Europe by 50 percent in grains and up to 550 percent in processed food. In the latter case, an increase in distance by 1 percent is expected to reduce intra-regional bilateral exports of processed food in Central America by 2.88 percent.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 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".