Adaptation strategy for the municipality of La Paz, Mexico: Multicriteria and cost-benefit analysis
Bibliographic record
Abstract
This paper identifies climate change adaptation measures in the municipality of La Paz, Mexico, based on the results of previous vulnerability analysis.To prioritize the specified measures the GIZ methodology is used base don milticriteria and cost-benefit analysis.The study comprises the following stages, firstly policies and instruments suggested by the academic team, were discussed and slightly modified at a meeting with the representatives of La Paz Municipality.Secondly, a survey was applied to the main directors and employees according to the criteria provided by the GIZ methodology.Thirdly, a Public Consultation Forum was organized with the main stakeholders of La Paz municipality (NGO, Business, professional associations), where the adaptation measures were ranked by thematic and multicriteria approach.This stage complemented the multicriteria analysis and presented the measures that ranked in first places.The last step consisted in the cost-benefit analysis that provided a further ranking to the measures and specified the short-term adaptation strategy for the city of La Paz.The main areas of this strategy are the following: I. Hydric resources; II.Coasts and Tourism; III.Fisheries and biodiversity; IV: Urban Planning and Infrastructure; V. Environmental education and research.Finally, we present the adaptation strategy for La Paz municipality based on the prioritized adaptation measures.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".