Coastal roads atlas. Reshaping daily infrastructures for coastline adaptation
Bibliographic record
Abstract
The widespread poor quality that characterizes many cities and urbanization today is often related to the outdoor spaces. These conditions are common situations in many contexts of our country but are intensified where spatial inequality and environmental vulnerability converge. Although declined in different territories these situations have at their center the complex space of the road. The road system not only contributes significantly to climate change but is also the main victim of the consequences related to these changes. The research investigates the role of the roads and parking spaces, interpreted as the main background of our everyday lives, in facilitating socio-ecological transition of most fragile territories. Particular attention is paid to the different vulnerabilities of Italian coastline and to the ways in which adaptation measures can be implemented to mitigate risks. The initial analyses are focused on developing methods to measure and evaluate the climatic, geophysical, and socio-economic vulnerabilities of coastal roads, which are rendered, through aggregated maps of quantitative and qualitative indicators, in an "Atlas of Coastal Roads". The Atlas is conceived as an operational tool, able to guide stakeholders to develop national and place-specific interpretations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.062 | 0.017 |
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".