Addressing the policy and business drivers of global freshwater biodiversity loss
Bibliographic record
Abstract
While they are important, local or catchment-level conservation efforts are by themselves unlikely to bend the curve of dramatic global-scale biodiversity loss in rivers, lakes, and freshwater wetlands. Other interventions will also be required, especially those that address the underlying socio-economic drivers of freshwater ecosystem degradation. Such drivers often manifest through decisions made at national or international scales by policymakers and business leaders in sectors including water resource management, agriculture and food production, energy generation, and inland fisheries. Few analyses have traced the impacts of such decisions on freshwater ecosystems and biodiversity, and the evidence base provides scant insight into effective approaches for addressing these underlying drivers. We begin to address this strategic knowledge gap by describing key policy and business sectors that the conservation and science communities should engage to address the systemic drivers of global freshwater biodiversity loss. Drawing on diverse experiences of international policy and business discourses and applied freshwater sciences, we provide an overview of international sector-specific risks and opportunities for freshwater conservation and propose potential priorities for engagement. We reflect on actions the freshwater sciences community can take to respond to these risks and opportunities, and we suggest priorities to shape a more systemic, driver-focused approach to freshwater conservation research that can support the integration of freshwater biodiversity considerations into policy and business decisions.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".