Transportation network analysis and hub identification for exporting agricultural products
Bibliographic record
Abstract
Agriculture products are one of the main income sources of developing countries. This study analyzes the road transport system (network) to reveal the network’s structural properties and identifies hubs that consolidate agricultural products to export to China via the China-Laos railway. Analysis of 20 provinces in the Northeast of Thailand has found that the network has 340 districts connected by 1,015 transport routes. The network is sparse, in which all districts cannot be connected to each other. The network has low connectivity efficiency but has high intra-connectivity among districts in the same province. In addition, the network has a modularity structure that can develop the communities. Hubs consolidating agricultural products of the region are Na Khu, Kuchinaria, Mueng Chiyaphum, Nam Phong, Na Wa, Mueang Mukdahan, Prasat and Rasi Salai. The findings of the study draw implications for the government, sector and exporters to design and improve their operations and services.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".