Climate Collaboratorium: A Transdisciplinary Approach to Modelling Groundwater Resources for Climate Adaptation in the Sorbian Community of Rietschen (Görlitz, Germany)
Bibliographic record
Abstract
The Climate Collaboratorium adopts a novel, transdisciplinary approach to address the interplay of climate change, groundwater dynamics, and socioeconomic factors. By combining advanced groundwater modelling with participatory methods, this project intends to develop actionable strategies for sustainable water management for the Sorbian community in Rietschen, Görlitz. This innovative methodology emphasizes collaboration between researchers and the community, ensuring that scientific insights align with local needs and values.Central to the project is the development of a state-of-the-art groundwater model, incorporating high-resolution spatial and temporal data, along with boundary conditions informed by literature, fieldwork, stakeholder inputs, and sensitivity analyses. This foundational model provides a baseline for understanding groundwater dynamics and serves as a platform for subsequent scenario simulations.In the second phase, the model will be adapted to evaluate climate change impacts on groundwater resources, integrating regional climate projections and recharge scenarios. Through workshops, community members will co-create socioeconomic scenarios and identify adaptation priorities. These priorities will guide the integration of local economic development plans and social behaviors into the model. To enhance community engagement, innovative methods such as theatrical performances will translate complex scientific findings into accessible and relatable formats.The final phase will simulate the complex interactions between climate impacts, land use changes, and socioeconomic behaviors under a range of scenarios. This approach enables the identification of key vulnerabilities and supports the development of robust, community-oriented adaptation strategies.The results will not only benefit the community of Rietschen but also provide transferable insights for similar communities facing groundwater management challenges. Also, comparable studies are going to be applied in Canada, the UK, and the USA to demonstrate the applicability of this approach, highlighting its relevance in diverse sociocultural and environmental contexts.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| 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".