Bibliographic record
Abstract
Abstract The freshwater environment is facing unprecedented global pressures. Unsustainable use of surface and groundwater is ubiquitous. Gross pollution is seen in developing economies, nutrient pollution is a global threat to aquatic ecosystems, and flood damage is increasing. Droughts have severe local consequences, but effects on food can be global. These current pressures are set in the context of rapid environmental change and socio‐economic development, population growth, and weak and fragmented governance. We ask what should be the role of the water science community in addressing water security challenges. Deeper understanding of aquatic and terrestrial environments and their interactions with the climate system is needed, along with trans‐disciplinary analysis of vulnerabilities to environmental and societal change. The human dimension must be fully integrated into water science research and viewed as an endogenous component of water system dynamics. Land and water management are inextricably linked, and thus more cross‐sector coordination of research and policy is imperative. To solve real‐world problems, the products of science must emerge from an iterative, collaborative, two‐way exchange with management and policy communities. Science must produce knowledge that is deemed to be credible, legitimate, and salient by relevant stakeholders, and the social process of linking science to policy is thus vital to efforts to solve water problems. The paper shows how a large‐scale catchment‐based observatory can be used to practice trans‐disciplinary science integration and address the Anthropocene's water problems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.028 | 0.016 |
| Insufficient payload (model declined to judge) | 0.026 | 0.002 |
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".