Ecological representation and conservation gaps of South Korea’s protected areas
Bibliographic record
Abstract
Summary The Convention on Biological Diversity, ratified by 196 countries including South Korea, aims to protect at least 30% of the world’s land, inland waters and marine areas by 2030 as part of the Kunming–Montreal Global Biodiversity Framework. Beyond increasing protected areas (PAs), promoting biodiversity by protecting different ecosystem types is crucial. We investigated whether South Korea’s PAs evenly cover various ecosystem types. We examined overlaps between the Korean Database of Protected Areas (KDPA) and the Korean adapted Ecosystem Typology (KET) map, which modified the International Union for Conservation of Nature (IUCN) Global Ecosystem Typology (GET) three-level ecosystem functional group map based on South Korea’s land cover. Compared to the biogeographical ecoregion map, the KET map provides finer ecological detail on representation within PAs and reveals the under-representation of human-influenced ecosystems; eight human-influenced ecosystem functional groups, including rice paddies and urban and industrial ecosystems that may contribute to biodiversity or cultural value, had <10% protection. The T2.2 deciduous temperate forest type dominates, covering 54.79% of PA area across 18 of 27 PA categories. This concentrated protection has led to up to 24 overlapping PA designations in certain locations. Expanding protection for under-represented ecosystems and diversifying governance could help South Korea align with global biodiversity goals.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".