Managing water resources under new climatic extremes in the Main river basin, Germany
Bibliographic record
Abstract
ARSINOE is an EU-funded project aimed at developing the methodological framework for the combination of System Innovation Approach (SIA) with the Climate Innovation Window (CIW) to create an ecosystem for climate change adaptation solutions. The project will work to create this ecosystem with a three-tier, approach show-cased in nine widely varied regions across Europe (demonstrators), as a proof of concept with regards to its applicability, replicability, potential and efficacy. Challenges for water resources management in Bavaria – the „Main River“ case study: • RATIONALE - Region is at risk for being pushed beyond its resilience threshold and will need a new level of responsiveness to cope with climate change. • BARRIERS - Limited awareness on severity of regional climate change impacts; Science-society-policy interface operates below capacity; CC related innovations and methodologies propagate too slow into practice; Responsibilities in preparing/responding to CC between private and public bodies remains vague and requires a harmonized structure. • Climate change induced changes on the hydrology in Bavaria (results from the ClimEx project; www.climex-project.eu) Acknowledgements: The CRCM5 was developed by the ESCER centre of Université du Québec à Montréal (UQAM; www.escer.uqam.ca) in collaboration with Environment and Climate Change Canada. We acknowledge Environment and Climate Change Canada's Canadian Centre for Climate Modelling and Analysis for executing and making available the CanESM2 Large Ensemble simulations used in this study, and the Canadian Sea Ice and Snow Evolution Network for proposing the simulations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".