Biomic river restoration: A new focus for river management
Bibliographic record
Abstract
Abstract River management based solely on physical science has proven to be unsustainable and unsuccessful, evidenced by the fact that the problems this approach intended to solve (e.g., flood hazards, water scarcity, and channel instability) have not been solved and long‐term deterioration in river environments has reduced the capacity of rivers to continue meeting the needs of society. In response, there has been a paradigm shift in management over the past few decades, towards river restoration. But the ecological, morphological, and societal benefits of river restoration have, on the whole, been disappointing. We believe that this stems from the fact that restoration overrelies on the same physical analyses and approaches, with flowing water still regarded as the universally predominant driver of channel form and structural intervention seen as essential to influencing fluvial processes. We argue that if river restoration is to reverse long‐standing declines in river functions, it is necessary to recognize the influence of biology on river forms and processes and re‐envisage what it means to restore a river. This entails shifting the focus of river restoration from designing and constructing stable channels that mimic natural forms to reconnecting streams within balanced and healthy biomes, and so levering the power of biology to influence river processes. We define this new approach as biomic river restoration .
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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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".