Measures to safeguard and restore river connectivity
Bibliographic record
Abstract
Freshwater connectivity and the associated flow regime are critical components of the health of freshwater ecosystems. When freshwater ecosystems are fragmented, the movements and flows of species, nutrients, sediments, and water are altered, changing the natural dynamics of freshwater ecosystems. The consequences of these changes include declines and loss of freshwater species populations and freshwater ecosystems, and alterations in the delivery of certain ecosystem services, such as fisheries, buffering of flood events, healthy deltas, recreational and cultural values, and others. Measures exist that can maintain and restore connectivity or mitigate against its loss in the face of constructed barriers or other habitat alterations. These measures include system-scale planning for energy and water resources that includes options for limiting loss of freshwater connectivity; putting in place protections for keeping critically important freshwater habitats connected; mitigating impacts on freshwater ecosystems via barrier design, fish passage, or implementation of environmental flows; and restoring freshwaters via barrier removal and reconnection of rivers, wetlands, and floodplains and via active management of groundwater recharge. We present case studies of measures applied in Europe, Asia, Africa, and the Americas and reflect on the next generation of innovation needed to further enhance and advance the implementation of restoration and protection and the mitigation of freshwater connectivity impacts.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".