Climate change adaptation cycle for pilot projects development in small municipalities: The northwestern Italian regions case study
Bibliographic record
Abstract
More than half of the European population live in small and medium size municipalities, where climate adaptation planning is an under-researched topic within the climate change field. Many constraints might hinder the implementation of adaptation pilot projects due to lack of economic, knowledge, and technical available resources. Local institutions find difficulties in building a coherent local adaptation planning and design processes with international and national frameworks. In this context, this article proposes a methodology based on the available international frameworks to support the small communities with the aim to implement adaptation pilot projects within different sectors. In doing so, this paper tests a climate change adaptation cycle for pilot projects development in small municipalities; the first in Italy for small municipalities under 20.000 inhabitants. The proposed methodology could lead local adaptation initiatives in climate change risk assessment by supporting the research communities in developing a coherent vision for the local territories and to identify proper oriented measures to enhance demonstrative pilot projects and to increase the level of resilience in small municipalities, avoiding maladaptation.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".