Cycling lanes and Stormwater Management: an integrated project. Montesilvano as a case study
Bibliographic record
Abstract
The frequency of flooding in Montesilvano has risen steadily in recent years. Linked to this phenomenon, the research agreement between the Department of Architecture in Pescara and the Town of Montesilvano includes the general objective of verifying whether the network of cycling lanes can help resolve this issue. Legislation, guidelines and best practices in his sector provide no useful indications. They are linked to a qualitative hypothesis whose priority in almost all cases focuses on creating the highest possible number of kilometres of safe, functional and intermodal cycling lanes. To identify operative references to the links between cycling lanes and stormwater management we must look at plans designed to contrast climate change. Many have a specific section dedicated to this theme: examples include Boston, Copenhagen, Melbourne, Ottawa and Philadelphia. Their comparison reveals that improving stormwater management requires multiple actions. Principal actions include: breaking free of sector-specific logics, integrated projects for the spaces of the network and associated areas and the recognition of the importance of the relationship with context. In Montesilvano, marked by two parallel north-south axes (the Parkway and the Waterfront) and its five perpendicular east-west lines (Grandi alberghi, via Strasburgo, via Marinelli, via Torrente Piomba, Palaroma), there is a need to identify areas ready to welcome a project that successfully combines bicycle mobility with stormwater treatment and management. This is the responsibility the research intends to assume in the near future.
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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 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".