Flexible and Adaptable Restoration: An Example from South Korea
Bibliographic record
Abstract
Abstract Ecological restoration is set to play a key role in mitigating biodiversity loss. While many restorationists worry about what to do about and what to call rapidly changing ecosystems (no‐analog, novel, or other terms), ecologists and managers in some parts of the world have avoided these controversies and proceeded with developing and implementing innovative restoration projects. We discuss examples from South Korea, including the Cheonggyecheon river project in Seoul and the new National Institute of Ecology, which combines scientific research, planted reference systems for future restoration, and an Ecorium for outreach and education. South Korea faces a range of restoration challenges, including managing even‐aged planted forests, major land use changes (especially urbanization) affecting valuable tidal flats, and fragmented landscapes caused by intensive land use and the fenced Demilitarized Zone (DMZ). The examples from South Korea provide insights that might guide future actions more broadly. These include flexible targets for restoration not based on historical precedents, considering ecosystem functions and functional trait diversity as well as species composition, creating model restoration projects, and managing adaptively.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".