Ecological Restoration in Laurentian Great Lakes Wetlands: A Literature Review
Bibliographic record
Abstract
The Laurentian Great Lakes basin, the largest surface freshwater ecosystem in the world, is heavily impacted by anthropogenic stressors including wetland degradation and loss. Ecological restoration has been identified as a tool to conserve and manage Great Lakes wetlands where studies undergo planning, action, and evaluation stages. A review of 57 studies (1967–2023) on Great Lakes coastal and inland wetlands was conducted to determine when and where ecological restoration is occurring, what approaches are being taken, and how effective they are. Restoration occurred most in coastal wetlands located in the United States of America and Lake Erie. The most implemented monitoring designs were control-impact and before-after-control-impact designs. A common ecological objective of restoration was altering species composition for invasive species management. Studies targeting changes in biological communities integrated ecological theory well within the three stages of restoration. Variable restoration effectiveness was detected, where a mix of ecological objectives were targeted and monitored through many study designs. Future restoration efforts would benefit from greater financial and monitoring investments (especially during the planning stage), continued integration of ecological theory, development of lake-specific benchmarks to assess restoration success, and a collaborative approach that includes practitioner knowledge and Traditional Ecological Knowledge.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".