The ecological success of river restoration in Newfoundland and Labrador, Canada: lessons learned
Bibliographic record
Abstract
Despite millions of dollars being spent annually to restore degraded river ecosystems, there exist relatively few assessments of the ecological effectiveness of projects. An evidence-based synthesis was conducted to describe river restoration activities in Newfoundland and Labrador. The synthesis identified 170 river restoration projects between 1949 and 2020. A practitioner’s survey was conducted on a subset of 91 projects to evaluate ecological success. When the perceived success of managers was compared to an independent assessment of ecological success, 82% of respondents believe the projects to be completely or somewhat successful whereas only 41% of projects were evaluated as ecologically successful through an independent assessment. Only 11% of practitioners’ evaluations used ecological indicators, yet managers of 66% of projects reported improvements in river ecosystems. This contradiction reveals a lack of the application of evidence to support value-based judgments by practitioners. Despite reporting that monitoring data were used in the assessment it is doubtful that any meaningful ecological assessment was conducted. If we are to improve the science of river restoration, projects must demonstrate evidence of ecological success to qualify as sound restoration. River restoration is a necessary tool to ensure the sustainability of river ecosystems. The assessment conducted in this study suggests that our approach to planning, designing, implementing, monitoring, and evaluating projects needs to improve. An integrated-systems view that gives attention to stakeholders’ values and scientific information concerning the potential consequences of alternative restoration actions on key ecosystem indicators is required.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".