Speaking the same language: Aligning project designations to clarify communication in restoration ecology
Bibliographic record
Abstract
As we enter the United Nations Decade of Ecosystem Restoration, the need to engage in restoration activities has never been greater. Included within this need is a requirement for clear communication between researchers, practitioners, policymakers, stakeholders, and community members. To facilitate the discussion and assessment of restorative activities, we propose two decision trees to differentiate between key restoration terms (Reclamation, Rehabilitation, Ecological Restoration, Rewilding, Landscape Restoration, Intra-Ecosystem Restoration, Reference Condition Restoration, and Ecological Reclamation) and to clarify how they relate to each other, based upon project scope and desired/intended project outcomes. Continued use of unclear terminology impedes practitioners or researchers from using the literature efficiently, to find precedents that could assist their current efforts. As such, increasing clarity of communications will ensure restoration is discussed within a framework of well-defined and agreed upon terms. It is our hope that this suggested framework will contribute to the ongoing and much needed terminology debate and help enhance cohesion on the use of key terms within the restoration focused literature. Given the increased focus upon restoration projects of any kind, especially during the UN's decade of ecosystem restoration, it is more important than ever that restoration practitioners speak the same language.
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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 | 0.399 | 0.390 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.013 | 0.048 |
| Scholarly communication | 0.030 | 0.050 |
| Open science | 0.007 | 0.022 |
| Research integrity | 0.016 | 0.019 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".