A Systematic Approach for Resilience Assessment in Road Transport Routes Involving Natural and Human Interruptions
Bibliographic record
Abstract
This work presents a new approach for resilience assessment in road transport routes interrupted by natural causes (rain, earthquakes) as well as by human influence (accidents). In this way, by knowing the state of preservation of the elements located within a road, particularly bridges, it will be possible to identify which are those with the highest priority to be attended for their conservation, repair, and even replacement, thus relating the cost of maintenance and the number of people benefited. To test this methodology, a case study was proposed. The proposed systematic approach is applied in the federal highway 15 route, which is an international route that passes through seven states of Mexico, ending in Alberta Canada. The study is limited to the Michoacan state of Mexico, which corresponds to 426 kilometers. This study identified the highway bridges within the road and analyzed their deterioration, the cost of repair, and the benefited inhabitants. The most significant scenarios were obtained in terms of repair cost and people benefited, identifying which bridges have priority to be served, having a more accurate decision and distribution of adequate financial resources.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".