Ecological restoration research in Canada: who, what, where, when, why, and how?
Bibliographic record
Abstract
Much has been achieved by research into ecological restoration as a nature-based solution to the destruction of ecosystems, particularly in Canada. We conducted a national-level synthesis of Canadian restoration ecology research to understand strengths and gaps. This synthesis answers the following questions: Who is studying restoration? What ecosystem types are studied? Where is restoration studied? Which themes has restoration research focused on? Why is restoration happening? And how is restoration monitored and evaluated? We employed systematic searching for this review. Our results show that restoration research is conducted mainly by academics. Forest, peatland, grassland, and lake ecosystem types were the most commonly studied. There was a concentration of research in four provinces (Ontario, Quebec, Alberta, and British Columbia). Research into restoration has changed its thematic focus over time from reforestation to climate change. Legislation was the most common reason given for restoration. Restoration research frequently documented results of less than 5 years of monitoring and included one category of response variable (e.g., plant response but not animal response). Future research could investigate the outcomes of restoration prompted by legislation. At the dawn of the UN Decade on Ecosystem Restoration, this work demonstrates Canada's momentum and provides a model for synthesis in other countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".