Ecological representation and conservation gaps of South Korea’s protected areas
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it