Managing the effects of multiple stressors on aquatic ecosystems under water scarcity. The GLOBAQUA project
Bibliographic record
Abstract
Water scarcity is a serious environmental problem in many European regions, and will likely increase in the near future as a consequence of increased abstraction and climate change. Water scarcity exacerbates the effects of multiple stressors, and thus results in decreased water quality. It impacts river ecosystems, threatens the services they provide, and it will force managers and policy-makers to change their current practices. The EU-FP7 project GLOBAQUA aims at identifying the prevalence, interaction and linkages between stressors, and to assess their effects on the chemical and ecological status of freshwater ecosystems in order to improve water management practice and policies. GLOBAQUA assembles a multidisciplinary team of 21 European plus 2 non-European scientific institutions, as well as water authorities and river basin managers. The project includes experts in hydrology, chemistry, biology, geomorphology, modelling, socio-economics, governance science, knowledge brokerage, and policy advocacy. GLOBAQUA studies six river basins (Ebro, Adige, Sava, Evrotas, Anglian and Souss Massa) affected by water scarcity, and aims to answer the following questions: how does water scarcity interact with other existing stressors in the study river basins? How will these interactions change according to the different scenarios of future global change? Which will be the foreseeable consequences for river ecosystems? How will these in turn affect the services the ecosystems provide? How should management and policies be adapted to minimise the ecological, economic and societal consequences? These questions will be approached by combining data-mining, field- and laboratory-based research, and modelling. Here, we outline the general structure of the project and the activities to be conducted within the fourteen work-packages of GLOBAQUA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".