Navigating Change: Enhancing Transportation Resilience in the Face of Climate Change - A Moroccan Case Study
Bibliographic record
Abstract
Climate change manifested through extreme weather events such as hurricanes, floods, wildfires, and rising sea levels, poses significant threats to transportation infrastructure, disrupts services, and raises safety concerns with dangerous repercussions on the broader economic sector. Drawing insights from the Moroccan Ministry of Equipment and Water on climate change and resilience, we delve into the unique challenges and vulnerabilities faced by the transportation sector in Morocco amidst a changing climate. The study explores advanced technologies, particularly artificial intelligence (AI) and computer vision, to mitigate the impacts of climate change on transportation safety. Emphasizing the need for foresighted measures, we explore applications such as computer vision for weather pattern recognition by analyzing satellite imagery and real-time monitoring via video feeds. This approach enables accurate and timely predictions of extreme weather events, facilitating improved preparedness and response strategies. The findings contribute to the broader discourse on climate change adaptation and sustainable development, offering a nuanced perspective based on the specific realities of the Moroccan transportation scene.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".