Evidence‐based restoration in the Anthropocene—from acting with purpose to acting for impact
Bibliographic record
Abstract
Abstract The recognition that we are in the distinct new epoch of the Anthropocene suggests the necessity for ecological restoration to play a substantial role in repairing the Earth's damaged ecosystems. Moreover, the precious yet limited resources devoted to restoration need to be used wisely. To do so, we call for the ecological restoration community to embrace the concept of evidence‐based restoration. Evidence‐based restoration involves the use of rigorous, repeatable, and transparent methods (i.e. systematic reviews) to identify and amass relevant knowledge sources, critically evaluate the science, and synthesize the credible science to yield robust policy and/or management advice needed to restore the Earth's ecosystems. There are now several examples of restoration‐relevant systematic reviews that have identified instances where restoration is entirely ineffective. Systematic reviews also serve as a tool to identify the knowledge gaps and the type of science needed (e.g. repeatable, appropriate replication, use of controls) to improve the evidence base. The restoration community, including both scientists and practitioners, needs to make evidence‐based restoration a reality so that we can move from best intentions and acting with so‐called “purpose” to acting for meaningful impact. Doing so has the potential to serve as a rallying point for reframing the Anthropocene as a so‐called “good” epoch.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.169 | 0.233 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.032 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.008 | 0.014 |
| 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".