Project house water: a novel interdisciplinary framework to assess the environmental and socioeconomic consequences of flood-related impacts
Bibliographic record
Abstract
Protecting our water resources in terms of quality and quantity is considered one of the big challenges of the twenty-first century, which requires global and multidisciplinary solutions. A specific threat to water resources, in particular, is the increased occurrence and frequency of flood events due to climate change which has significant environmental and socioeconomic impacts. In addition to climate change, flooding (or subsequent erosion and run-off) may be exacerbated by, or result from, land use activities, obstruction of waterways, or urbanization of floodplains, as well as mining and other anthropogenic activities that alter natural flow regimes. Climate change and other anthropogenic induced flood events threaten the quantity of water as well as the quality of ecosystems and associated aquatic life. The quality of water can be significantly reduced through the unintentional distribution of pollutants, damage of infrastructure, and distribution of sediments and suspended materials during flood events. To understand and predict how flood events and associated distribution of pollutants may impact ecosystem and human health, as well as infrastructure, large-scale interdisciplinary collaborative efforts are required, which involve ecotoxicologists, hydrologists, chemists, geoscientists, water engineers, and socioeconomists. The research network "project house water" consists of a number of experts from a wide range of disciplines and was established to improve our current understanding of flood events and associated societal and environmental impacts. The concept of project house and similar seed fund and boost fund projects was established by the RWTH Aachen University within the framework of the German excellence initiative with support of the German research foundation (DFG) to promote and fund interdisciplinary research projects and provide a platform for scientists to collaborate on innovative, challenging research. Project house water consists of six proof-of-concept studies in very diverse and interdisciplinary areas of research (ecotoxicology, water, and chemical process engineering, geography, sociology, economy). The goal is to promote and foster high-quality research in the areas of water research and flood-risk assessments that combine and build off-laboratory experiments with modeling, monitoring, and surveys, as well as the use of applied methods and techniques across a variety of disciplines.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".