A systematic review of the global freshwater mussel restoration toolbox
Bibliographic record
Abstract
Abstract Halting the global decline of freshwater mussels requires an improved understanding of the restoration approaches currently in use and the identification of shortcomings to inform the development of new ones. This article reviews published evaluations of restoration efforts on the ground and those identified or proposed in the literature. This review found few reports of the effectiveness of restoration measures, and of those reported there was strong temporal and geographical bias. Reports were mostly recent and took place within mussel‐diverse regions of the Global North (i.e. North America, Europe). Efforts focused on population support encompassed relocation, translocation and population augmentation from captive breeding, and potential restoration approaches emphasized planning and prioritization. It was challenging to assess restoration success owing to differences in success metrics and varied lengths of post‐restoration monitoring. Some measures were inherently more species specific, such as population support, but some articles suggested that the use of multiple measures may allow more species to be addressed by restoration efforts. Consistent documentation and reporting of restoration measures – including failed ones – are needed to advance freshwater mussel restoration. In addition, continued work is needed to accelerate and better align the development and use of restoration approaches for freshwater mussels. Reporting of unsuccessful or partially successful attempts does not adequately reflect the long life histories of freshwater mussels. Investigation of the contexts where approaches have been effective (even partially), and concerted efforts to implement combinations of measures at a catchment scale are critical for unionids globally. As such, recommendations are posed to aid integration of research and practice to advance and further develop freshwater mussel 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 | 0.013 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.021 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".