An integrated and shared approach to sea of the regional town master plan of Sicily
Bibliographic record
Abstract
The work addresses one of the most important problems in contemporary environmental land planning.Already existing procedures, in fact, must now conform to the new requirements imposed by the recent national and international regulations and standards that call for a more conscious approach to the use of natural resources.Strategic Environmental Assessment (SEA), for example, takes into account, the different effects of a plan in various fields such as the environment, the economy and the development.This obviously calls for global analysis tools that could help administrators, stakeholders and technicians in the decision-making process of such complex systems.SEA of the Town Master Plan of the Sicilian region is here utilized for demonstrating the effectiveness of a continuously concerted action with the stakeholders in the decision-making processes involving wide and complex territories.For this purpose, the Dashboard of Sustainability is applied to the Sicilian Town Master Plan by comparing the performances of nine Sicilian provinces in terms of different policy scenarios, through a consideration of the effects on the environment, mobility, society and town planning issues.This consultation procedure results in a very effective tool for politically ranking, within a shared frame, different alternatives referring to land developing interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".