The intervention continuum in restoration ecology: rethinking the active–passive dichotomy
Bibliographic record
Abstract
The distinction often made between active and passive restoration approaches is a false dichotomy that persists in much research, policy, and financial structures today. We explore the contradictions imposed by this terminology and the merits of replacing this dichotomy with a continuum‐based intervention framework. In practice, the main distinction between “passive” and “active” restoration lies primarily in the timing and extent of human interventions. We apply the intervention continuum framework to forest, grassland, stream, and peatland ecosystems, emphasizing that a range of restoration approaches within the scope of ecological or ecosystem restoration are typically employed in most projects, and all can contribute to the recovery of native ecosystems and prevention of further degradation. As restoration is fundamentally about the recovery of ecosystems, eliminating human sources of degradation is essential to enable ecosystem recovery processes, regardless of subsequent interventions that may be needed to assist recovery. Our review of restoration practices involving different levels of intervention highlights the benefits of recognizing a broader suite of restoration interventions in the financial and policy frameworks that currently underpin restoration activity. Effective restoration interventions emerge from an understanding of nature's intrinsic recovery potential and overcoming specific obstacles that limit this potential.
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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 | 0.065 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.067 |
| Scholarly communication | 0.013 | 0.024 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.008 | 0.012 |
| 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".