A Comprehensive Index to Evaluate Non-motorized Accessibility to Port-Cities
Bibliographic record
Abstract
Guaranteeing permeability between port and city areas is a complex issue since, in recent years, there have been deep changes, due to the evolution of maritime transport, traffic volumes and port infrastructures, which have profoundly influenced the port-city relationship.The recent growing of the cruise and yachting sectors, however, highlighted the huge possibilities of economic development related to these activities, especially in the areas of the cities located close the ports.In this light, it is essential to ensure good accessibility to the city from the port (and vice versa) and to ensure that urban routes within the areas close to the port offer visitors a pleasant experience, in terms of walkability and opportunities.In this paper a Walkability Comprehensive Index to evaluate the quality of facilities in proximity to port areas will be presented; the index will include evaluation concerning the accessibility of the facilities, their Level of Service and the Places of Interests for visitors entering the city from the port.The calculation of the index is based on a spatial analysis with data obtained through field surveys and by an open source approach.The methodology is applied to the case study of the Port of Catania, a coastal city located in the south of Italy.The comprehensive index provides with information on each arc of the road network, offering an aid for decision-makers in prioritizing intervention for the improvement of non-motorized infrastructure within the interface area between the port and the city.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".