Strengthen the European collaborative environmental research to meet European policy goals for achieving a sustainable, non-toxic environment
Bibliographic record
Abstract
Abstract To meet the United Nations (UN) sustainable development goals and the European Union (EU) strategy for a non-toxic environment, water resources and ecosystems management require cost-efficient solutions for prevailing complex contamination and multiple stressor exposures. For the protection of water resources under global change conditions, specific research needs for prediction, monitoring, assessment and abatement of multiple stressors emerge with respect to maintaining human needs, biodiversity, and ecosystem services. Collaborative European research seems an ideal instrument to mobilize the required transdisciplinary scientific support and tackle the large-scale dimension and develop options required for implementation of European policies. Calls for research on minimizing society’s chemical footprints in the water–food–energy–security nexus are required. European research should be complemented with targeted national scientific funding to address specific transformation pathways and support the evaluation, demonstration and implementation of novel approaches on regional scales. The foreseeable pressure developments due to demographic, economic and climate changes require solution-oriented thinking, focusing on the assessment of sustainable abatement options and transformation pathways rather than on status evaluation. Stakeholder involvement is a key success factor in collaborative projects as it allows capturing added value, to address other levels of complexity, and find smarter solutions by synthesizing scientific evidence, integrating governance issues, and addressing transition pathways. This increases the chances of closing the value chain by implementing novel solutions. For the water quality topic, the interacting European collaborative projects SOLUTIONS, MARS and GLOBAQUA and the NORMAN network provide best practice examples for successful applied collaborative research including multi-stakeholder involvement. They provided innovative conceptual, modelling and instrumental options for future monitoring and management of chemical mixtures and multiple stressors in European water resources. Advancement of EU water framework directive-related policies has therefore become an option.
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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 | 0.090 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.011 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".