Assessing vulnerabilities as a step toward climate change induced hazard preparedness
Bibliographic record
Abstract
Increasingly, the consequences of climate change are recognized not only on a national, but also on the regional and local levels.More and more local administrations ask if and which measures should be implemented to be prepared concerning climate change induced hazards, such as flooding, soil erosion, or drought and heat periods in rural and/or urban environments.Within the framework of a project carried out between 2013 and 2016, a local climate change adaptation strategy has been developed in a pilot region in middle Europe.Taking into account as many stakeholders, or actors from different sectors as possible, measures to adapt to climate change were defined based on the previous assessment of specific vulnerabilities.However, vulnerability assessment has been supported by the analysis of vast amount of spatial datasets using online geographic information services that were implemented as part of the project.Based on such technologies, as well as a web-based open forum, actors and the public were enabled to participate actively in the vulnerability assessment and especially concerning the definition of climate change adaptation measures.The participation process under explicit consideration of diverse relevant actors has lead to improved acceptance, and therefore more sustainable decisions about measures.The benefits resulting from using open participation tools, including geographic information technologies and communication support, to identify and evaluate vulnerabilities will be discussed.This is linked to the goals of a follow-up project that starts in 2017, called 'BebeR', in which a special focus is on soil erosion due to increasing heavy rainfall events accompanied by flooding.The computer based support during the prioritization and implementation of measures to mitigate potential threats will be considered and conclusions be drawn.
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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".