Environmentally Conscious Least Cost Multi-Criteria Decision Making for Modeling Railway Network for Eritrea
Bibliographic record
Abstract
A proper development of railway transportation is critical for sustainable socio-economic and environmental benefits. Nonetheless, in Eritrea, a country that once had a vibrant railway system, it is currently nonfunctional. There are also ambitious sectoral investments in neighboring Ethiopia and Sudan, pressing Eritrea to revitalize its decimated railway transportation system. Therefore, this study used population settlement pattern, environmental and physical criteria as inputs to a least-cost multi-criteria decision-making for modeling the railway network for Eritrea. Accordingly, seven railway routes, covering a total of 1, 610 km distance, were modeled. Four are running in the north-to-south directions, while three are running in the east-to-west directions. This railway network will give services to 5 out of 6 Eritrean’s administrative regions, where 93% of the estimated 5.8 million people reside. Additionally, it will service the 21 urban centers, where an estimated population of 1,658,749 live; approximately 70% of Eritrea’s urban population. Besides, it shall boost regional integration, peace, and development thereof, in an otherwise conflict-ravaged geographical 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".